Delivery of Smoking Cessation Services and Cessation Attempts Across a Public, Safety-Net Primary Care System

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Suen, Henry Rafferty, Thao Le, Kara Chung, Elana Straus, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-115150/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract Background: Smoking cessation rates are low in safety-net settings, contributing to high smoking-related morbidity and mortality. Understanding factors associated with cessation attempts can inform interventions. Objective: To evaluate factors associated with smoking cessation attempts. Design: Retrospective analysis using electronic health record (EHR) data on individuals with at least three primary care encounters from 2016 to 2019 in the San Francisco Health Network (SFHN), a network of clinics serving publicly insured and uninsured residents in San Francisco. Participants: Patients engaged in primary care in the San Francisco Health Network. Main Measures: The outcome was recent cessation attempt, defined as change in smoking status from “current smoker” at the index visit to “former smoker” at visit 2 or 3. We measured demographics, tobacco-related comorbidities, and cessation treatment characteristics (i.e., counseling and pharmacotherapy). To better characterize subpopulations that may benefit from targeted interventions, we described characteristics of smokers with hypertension, depression, diabetes, or HIV. Key Results: Of the 51,554 adults identified across 15 SFHN primary care clinics, 11,622 (22.7%) were current smokers. Approximately 26% of smokers made a recent cessation attempt. Medical assistant (90%) and provider counseling (73%) rates were high, while behavioral assistant counseling rate (17%) was low. All counseling types had lower odds of cessation attempts in multivariable analysis. Smokers with depression (AOR 1.18, 95%CI 1.05-1.33) and ischemic heart disease (AOR 1.36, 95%CI 1.06-1.74) had higher odds of attempts. Among comorbidity groups, cessation attempts ranged from 21-26%, and smokers with HIV received the lowest rates of cessation counseling. Conclusions: Although rates of basic cessation counseling were high, efforts were associated with lower odds of making a cessation attempt. Using intensive interventions to target populations with comorbidities could be opportunities to increase cessation engagement. Health Economics & Outcomes Research Infectious Diseases Health Policy smoking cessation electronic medical records primary care safety-net clinics quality improvement Figures Figure 1 Introduction Despite decades of efforts to reduce tobacco use in the United States (US), cigarette smoking accounts for one in five deaths annually and remains a leading cause of preventable death. 1 The prevalence of smoking and burden of tobacco-caused diseases further remain disproportionately high in individuals with low socioeconomic status, mental illness, substance use disorders, and communities of color. 2 , 3 Safety-net health systems that provide care for populations with high smoking rates are therefore uniquely poised to provide smoking cessation services, though many competing priorities and limited resources make providing cessation care challenging. Our prior work, using a PRECEDE-PROCEED evaluation framework, explored enabling factors and barriers to delivery of cessation services within a subset of safety-net clinics. 7 We found that due to competing priorities, clinic staff trained in offering cessation counseling (e.g., behavioral assistants) were not able to meet the demands of “warm hand offs” for counseling during encounters, requiring rescheduling visits that resulted in low follow up. Lack of both coordination of cessation services and ownership of counseling by clinical workforces also posed barriers to successful delivery of cessation services. 7 Less is known about patient-level factors associated with smoking cessation attempts and receipt of cessation services within safety-net populations who have comorbid chronic physical and mental health conditions. This study expands on prior work by using the electronic health record (EHR) to identify factors associated with smoking cessation attempts among all primary care clinics in the San Francisco Health Network (SFHN), the largest network of primary care clinics, specialty clinics, acute care hospitals, and behavioral health clinics serving publicly insured and uninsured patients in San Francisco. In recent years, the SFHN has undertaken various quality improvement initiatives to improve smoking cessation care across the entire health system. Understanding patient-level characteristics and factors associated with cessation attempts can enable opportunities to develop future interventions within SFHN and other urban safety-net systems. We hypothesized that delivery of cessation services, including any counseling from medical assistants, providers, and behavioral assistants, as well as pharmacotherapy, would be associated with cessation attempts. Methods Study setting and population We extracted patient-level EHR data on smoking status and delivery of smoking cessation services from 15 primary care clinics across SFHN (Appendix 1). Four of the 15 clinics (Clinics 2–6) are academic primary care practices housed within a university-affiliated public hospital, with three general primary care clinics and one clinic focusing on care for patients living with HIV. The remaining 11 clinics are community-based clinics dispersed across San Francisco with varying patient demographics. Clinic 1 is located in the downtown area and provides care predominantly to people experiencing homelessness, whereas other clinics serve a higher proportion of Black, Latinx, Asian, and other racial/ethnic communities. At each clinic visit, medical assistants (i.e., frontline medical staff) screen patients for smoking status as “current smoker,” “past smoker,” or “never smoker” and input their status into the EHR. To be consistent with prior literature and clinical practice, we refer to “past smokers” as “former smokers” for the remainder of the study. 7 For current smokers, medical assistants can refer to one or more cessation resources including those specific to the clinic (e.g., on-site smoking cessation groups or counseling by the behavioral assistants) and/or general resources (e.g., the California Smoker’s Helpline). 14 Providers and medical assistants during the encounter may also refer to behavioral assistants for counseling. Behavioral assistants are ancillary staff trained to offer smoking cessation coaching, and they offer on-site or telephone counseling to patients. Patients can also receive counseling from their provider and/or initiate smoking cessation pharmacotherapy through a prescription from their provider or a pharmacist during the clinical encounter. This study was approved by the University of California, San Francisco Committee on Human Research (#18-26398). Study Design The study utilized the PRECEDE-PROCEED model as its theoretical framework, an implementation science model used for assessing the health needs within a public health program, and for designing, implementing, and evaluating programs to meet these needs. 15 In our prior work, we conducted social, epidemiological, and ecological assessments to identify predisposing and reinforcing factors (PRECEDE) to increase delivery of cessation services. 7 We found that provision of medical services using team-based care models is feasible, and that the EHR could be a useful tool to facilitate delivery of cessation care. Here, we explore potential Policy, Regulatory, and Organizational (PROCEED) factors in order to facilitate the implementation of system-level interventions to increase delivery of cessation services and evaluation of such interventions. We extracted data from the EHR on any patient of the 15 clinic sites who had at least one recorded smoking status and at least three unique primary care encounters between May 2016 to May 2019. The index visit was the first encounter closest to the study’s start date. Current smokers reported smoking at the index visit, whereas non-smokers were either former smokers or never smokers. We extracted data from the EHR using i2i Tracks ( http://www.i2isys.com/p/i2itracks ) software and Structured Query Language (SQL). We extracted patient level factors including age, gender, primary language, race/ethnicity, health insurance type, and smoking-related comorbidities from the EHR. Health insurance was categorized as either Medicare, Medicaid (named “Medi-Cal” in California), Healthy San Francisco/Healthy Workers (a county-based program providing medical care to low-income adults), private, other coverage, or uninsured. Smoking-related health comorbidities were identified using ICD-9 and ICD-10 codes listed as diagnoses within the patient’s medical record (Appendix 2). Data Analysis We used descriptive statistics to summarize the data, with frequencies and percentages for categorical variables and means with standard deviations (SD) for continuous variables. We compared current smokers and non-smokers based on smoking status at the index visit. We used Pearson’s chi-square test for categorical variables and the t-test for continuous variables when comparing demographic characteristics, health insurance type, comorbidities, clinic, and referral characteristics. We evaluated factors associated with making a recent smoking cessation attempt. Recent smoking cessation attempt was defined as a transition in smoking status from current smoker at the index to former smoker either at visit 2 or 3. We chose independent variables previously shown to be associated with smoking cessation attempts, including demographics, health insurance type, clinic, visit number, comorbidities, and receipt of cessation services including counseling (medical assistant counseling, provider counseling, behavioral assistant counseling) and pharmacotherapy. 7 We examined the multivariable association between recent cessation attempts and covariates using generalized estimating equations (GEE), accounting for nesting patients within clinics with an exchangeable correlation structure. We further characterized rates of recent cessation attempts as well as the relapse rates by clinic. We defined a relapse as a change in smoking status from former smoker at visit 2 to current smoker at visit 3. To better understand disparities in delivery of smoking cessation services and delineate which subpopulations may benefit from more intensive efforts, we described characteristics within patient groups with various comorbidities ( i.e. , hypertension, diabetes, depression, and/or HIV), including their demographics, health insurance type, clinic, receipts of smoking cessation counseling and pharmacotherapy, and cessation attempts. All analyses were completed using SAS version 9.4. Results Individual Characteristics among Smokers and Non-Smokers The sample included 51,554 individuals from 15 clinics, of whom 11,622 (23%) were current smokers (Table 1 ). Approximately 37% of current smokers and 43% of non-smokers came from the four academic, hospital-based clinics. Compared to non-smokers, current smokers were more likely to be younger (mean age 50.3 years +/- SD 13.9 vs 52.2 years +/- SD 16.5), male (68% vs 42%), and White (27% vs 16%) or Black/African American (32% vs 10%). Current smokers were also more likely to be English-speaking (81% vs 51%) and had higher prevalence of asthma (10% vs 8%), COPD (14% vs 4%), depression (32% vs 25%), HIV (12% vs 4%), and heart failure (4% vs 3%). Table 1 Demographic, Comorbidities and Referral Characteristics among Smokers and Non-smokers in San Francisco Health Network clinics (N = 51,203) Mean (SD) or n (%) p-value Current smokers (N = 11,622) Non-smokers (N = 39,581) Age 50.3 (13.9) 52.2 (16.5) < .0001 Gender < .0001 Male 7,921 (68.2%) 16,516 (42%) Female 3,701 (32%) 23,065 (58%) Race/ethnicity < .0001 Hispanic 2,034 (18%) 12,965 (33%) American Indian/Alaska Native 119 (1%) 187 (0.5%) Asian 1,859 (16%) 13,119 (34%) Black/African American 3,622 (32%) 3,973 (10%) Native Hawaiian/Pacific Islander 150 (1%) 352 (0.9%) White 3,096 (27%) 6,236 (16%) Other 564 (5%) 2,184 (6%) Language < .0001 English 9,340 (81%) 20,179 (51%) Spanish 958 (8%) 9,526 (24%) Cantonese/Mandarin/Chinese 884 (8%) 7,283 (18%) Vietnamese 80 (0.7%) 634 (2%) Russian 125 (1%) 585 (1%) Tagalog 43 (0.4%) 353 (0.9%) Korean 24 (0.2%) 213 (0.5%) Arabic 24 (0.2%) 121 (0.3%) Other 58 (0.5%) 499 (1%) Insurance < .0001 Healthy San Francisco 499 (5%) 3,198 (9%) Medi-Cal 6,177 (59%) 16,552 (47%) Medicare 2,101 (20%) 7,060 (20%) Private 112 (1%) 479 (1%) Other coverage 1,190 (11%) 6,967 (20%) Uninsured 335 (3%) 816 (2%) Comorbidities Asthma 1,102 (9%) 3,126 (8%) < .0001 Chronic obstructive lung disease 1,567 (13%) 1,572 (4%) < .0001 Depression 3,737 (32%) 9,924 (25%) < .0001 Diabetes 1,863 (16%) 8,341 (21%) < .0001 HIV 1,424 (12%) 1,715 (4%) < .0001 Hyperlipidemia 2,448 (21%) 11,843 (30%) < .0001 Hypertension 4,309 (37%) 15,197 (38%) 0.01 Ischemic heart disease 627 (5%) 2,206 (6%) 0.46 Heart failure 491 (4%) 1,286 (3%) < .0001 Kidney disease 1,114 (10%) 4,242 (11%) < .001 Clinic < .0001 Clinic 1 1,949 (17%) 1,512 (4%) Clinic 2 52 (0.5%) 413 (1%) Clinic 3 1,562 (13%) 8,385 (21%) Clinic 4 1,006 (9%) 1,281 (3%) Clinic 5 1,637 (14%) 6,892 (17%) Clinic 6 712 (6%) 2,910 (7%) Clinic 7 28 (0.2%) 14 (0.04%) Clinic 8 645 (6%) 4,413 (11%) Clinic 9 380 (3%) 764 (2%) Clinic 10 24 (0.2%) 12 (0.03%) Clinic 11 732 (6%) 2,570 (6%) Clinic 12 592 (5%) 3,244 (8%) Clinic 13 487 (4%) 1,531 (4%) Clinic 14 634 (5%) 3,489 (9%) Clinic 15 1,182 (10%) 2,149 (5%) Referral characteristics Medical assistant counseling 10,426 (90%) — Provider counseling 8,467 (73%) — Behavioral assistant counseling 2,031 (17%) — Pharmacotherapy 1,419 (12%) — Table 2 Logistic regression model of factors associated with making a recent smoking cessation attempt among Smokers in San Francisco Health Network clinics Unadjusted OR Adjusted OR OR (95%CI) p value AOR (95%CI) p value Age 0.98 (0.97, 0.98) < 0.001 0.99 (0.98, 0.99) < 0.001 Gender Male (ref.) — — — — Female 1.21 (1.09, 1.34) < 0.001 1.25 (1.10, 1.41) < 0.001 Race/ethnicity White (ref.) — — — — American Indian/Alaska Native 1.30 (0.83, 2.05) 0.25 1.82 (1.10, 3.02) 0.02 Asian 0.88 (0.74, 1.03) 0.11 1.02 (0.82, 1.28) 0.85 Black/African American 1.00 (0.87, 1.14) 0.99 1.03 (0.88, 1.21) 0.70 Hispanic 1.90 (1.64, 2.20) < 0.001 1.31 (1.08, 1.60) 0.006 Native Hawaiian/Pacific Islander 1.28 (0.83, 1.98) 0.27 0.98 (0.59, 1.65) 0.95 Other 1.50 (1.18, 1.91) < 0.001 1.05 (0.80, 1.38) 0.71 Language English (ref.) — — — — Arabic 1.97 (0.71, 5.44) 0.19 1.29 (0.41, 4.03) 0.66 Cantonese/Mandarin/Chinese 0.53 (0.42, 0.66) < 0.001 0.72 (0.52, 1.01) 0.06 Korean 0.20 (0.03, 1.50) 0.12 0.27 (0.03, 2.21) 0.22 Other 1.11 (0.28, 1.60) 0.36 0.67 (0.28, 1.60) 0.36 Russian 0.98 (0.55, 2.23) 0.78 0.98 (0.55, 1.76) 0.95 Spanish 2.30 (1.96, 2.69) < 0.001 1.44 (1.15, 1.81) 0.001 Tagalog 0.66 (0.24, 1.85) 0.43 0.86 (0.24, 3.10) 0.82 Vietnamese 1.00 (0.58, 1.72) 0.99 0.78 (0.43, 1.43) 0.42 Insurance type Medicare (ref.) — — — — Healthy San Francisco 2.18 (1.71, 2.79) < 0.001 0.81 (0.60, 1.09) 0.16 Medi-Cal 1.28 (1.12, 1.46) < 0.001 0.86 (0.73, 1.01) 0.06 Private 1.23 (0.72, 2.13) 0.45 0.62 (0.33, 1.17) 0.14 Other 1.02 (0.84, 1.24) 0.86 0.87 (0.69, 1.09) 0.22 Uninsured 1.08 (0.77, 1.52) 0.65 0.75 (0.52, 1.09) 0.13 Clinic Clinic 1 (ref.) — — — — Clinic 2 8.52 (3.13, 23.19) < 0.001 4.82 (1.58, 14.73) 0.006 Clinic 3 2.78 (2.31, 3.34) < 0.001 2.43 (1.96, 3.02) < 0.001 Clinic 4 1.38 (1.11, 1.73) 0.005 1.14 (0.79, 1.63) 0.49 Clinic 5 2.85 (2.37, 3.42) < 0.001 2.58 (2.08, 3.20) < 0.001 Clinic 6 2.55 (2.01, 3.22) < 0.001 2.90 (2.22, 3.77) < 0.001 Clinic 7 0.87 (0.65, 1.16) 0.33 1.57 (1.08, 2.28) 0.02 Clinic 8 0.89 (0.20, 4.02) 0.88 0.64 (0.12, 3.35) 0.59 Clinic 9 0.88 (0.61, 1.25) 0.47 1.19 (0.78, 1.81) 0.41 Clinic 10 1.29 (0.44, 3.78) 0.64 1.25 (0.43, 3.66) 0.68 Clinic 11 1.63 (1.28, 2.07) < 0.001 2.03 (1.55, 2.65) < 0.001 Clinic 12 1.57 (1.21, 2.05) < 0.001 2.38 (1.74, 3.26) < 0.001 Clinic 13 1.61 (1.20, 2.16) 0.002 1.76 (1.27, 2.43) < 0.001 Clinic 14 2.02 (1.57, 2.61) < 0.001 1.89 (1.41, 2.54) < 0.001 Clinic 15 1.48 (1.19, 1.85) < 0.001 1.30 (1.01, 1.68) 0.04 Visit Visit 2 (ref.) — — — — Visit 3 0.69 (0.62, 0.76) < 0.001 0.77 (0.69, 0.86) < 0.001 Comorbidities Asthma 0.99 (0.84, 1.17) 0.91 1.03 (0.85, 1.24) 0.78 Chronic obstructive lung disease 0.68 (0.58, 0.78) < 0.001 1.02 (0.85, 1.23) 0.82 Depression 1.02 (0.92, 1.13) 0.70 1.18 (1.05, 1.33) 0.006 Diabetes 0.98 (0.86, 1.12) 0.75 1.00 (0.85, 1.18) 0.97 HIV 0.76 (0.65, 0.89) < 0.001 1.21 (0.90, 1.64) 0.21 Hyperlipidemia 0.85 (0.75, 0.96) 0.007 1.17 (1.00, 1.36) 0.05 Hypertension 0.77 (0.70, 0.86) < 0.001 1.03 (0.90, 1.18) 0.62 Ischemic heart disease 1.00 (0.81, 1.23) 0.98 1.36 (1.06, 1.74) 0.02 Heart failure 1.03 (0.82, 1.30) 0.79 1.26 (0.96, 1.64) 0.10 Kidney disease 0.90 (0.77, 1.06) 0.22 1.06 (0.87, 1.29) 0.58 Counseling and pharmacotherapy Medical assistant counseling 0.36 (0.32, 0.40) < 0.001 0.35 (0.31, 0.40) < 0.001 Provider counseling 0.44 (0.40, 0.49) < 0.001 0.66 (0.58, 0.76) < 0.001 Behavioral assistant counseling 0.63 (0.55, 0.72) < 0.001 0.83 (0.71, 0.98) 0.03 Pharmacotherapy 0.72 (0.62, 0.83) < 0.001 0.85 (0.71, 1.02) 0.08 Receipt of counseling and pharmacotherapy Among current smokers, 90% received medical assistant counseling during the study period, 73% received provider counseling, 17% received behavioral assistant counseling, and 12% received pharmacotherapy. Rates of Cessation Attempts Within the cohort of current smokers at the index visit, 26% made a recent smoking cessation attempt at either visit 2 or 3 (Fig. 1 ). Academic, hospital-based clinics had some of the highest cessation attempt rates. Among those who had made a cessation attempt at visit 2, on average 45% relapsed at visit 3 (range 0–50%) (Appendix 3). Factors Associated with Recent Smoking Cessation Attempts All forms of smoking cessation counseling were significantly associated with lower odds of making a cessation attempt. Receipt of smoking cessation pharmacotherapy did not reach statistical significance. Among patient factors, only older age was associated with lower odds of making a cessation attempt (Adjusted Odds Ratio [AOR] 0.99, 95% CI 0.98–0.99). Factors associated with higher odds of making a cessation attempt included female gender (AOR 1.25, 95% CI 1.10–1.41), American Indian/Alaskan Native (AOR 1.82, 95% CI 1.10–3.02) and Latinx/Hispanic ethnicity compared to Non-Hispanic White (AOR 1.31, 95% CI 1.08–1.60), or Spanish as the primary language (AOR 1.44, 95% CI 1.14–1.81). Those with depression (AOR 1.18, 95% CI 1.05–1.33) and ischemic heart disease (AOR 1.36, 95% CI 1.06–1.74) also had higher odds of making a cessation attempt. Across clinics, compared to Clinic 1 (a downtown clinic predominantly serving those experiencing homelessness), all academic, hospital-based clinics except Clinic 4 were associated with making recent cessation attempts and had some of the highest odds ratios across all 15 clinics. Demographic and Referral Characteristics among Smokers with Specific Comorbidities Among current smokers with available EHR comorbidity data, we stratified patients based on the presence of comorbidities of hypertension (N = 3,441), diabetes (N = 1,446), depression (N = 2,903), and HIV (N = 1,140) (Table 3 ). Current smokers in all comorbidity groups were on average middle aged (though those with HIV were slightly younger) and predominantly male. Table 3 Demographic and Referral Characteristics among Smokers with Hypertension, Diabetes, Depression, HIV in San Francisco Health Network clinics Mean (SD) or n (%) Smokers with Hypertension (N = 3,441) Smokers with Diabetes (N = 1,446) Smokers with Depression (N = 2,903) Smokers with HIV (N = 1,140) Age 58.5 (10.3) 57.6 (10.5) 51.5 (13.0) 48.0 (11.7) Gender Male 2,342 (68%) 1,025 (71%) 1,793 (62%) 975 (86%) Female 1,099 (32%) 421 (29%) 1,110 (38%) 165 (14%) Race/ethnicity Hispanic 384 (11%) 249 (17%) 472 (16%) 201 (18%) American Indian or Alaska Native 28 (1%) 6 (0.4%) 39 (1%) 30 (3%) Asian 551 (16%) 316 (22%) 283 (10%) 58 (5%) Black/African American 1,501 (44%) 533 (37%) 982 (34%) 346 (31%) Native Hawaiian or other Pacific Island 44 (1%) 33 (2%) 30 (1%) 3 (0.3%) White 761 (22%) 238 (17%) 935 (33%) 428 (38%) Other 148 (4%) 62 (4%) 123 (4%) 57 (5%) Insurance Healthy San Francisco 84 (3^) 70 (6%) 78 (3%) 21 (2%) Medi-Cal 1,494 (50%) 626 (50%) 1582 (63%) 620 (65%) Medicare 977 (33%) 359 (29%) 608 (24%) 235 (24%) Private 20 (1%) 11 (1%) 26 (1%) 15 (2%) Other coverage 329 (11%) 144 (12%) 161 (6%) 34 (4%) Uninsured 89 (3%) 30 (2%) 54 (2%) 30 (3%) Clinic Clinic 1 705 (20%) 257 (18%) 628 (22%) 206 (18%) Clinic 2 3 (0.1%) 0 9 (0.3%) 0 Clinic 3 399 (12%) 170 (12%) 327 (11%) 12 (1%) Clinic 4 187 (5%) 46 (3%) 322 (11%) 783 (69%) Clinic 5 465 (14%) 253 (18%) 352 (12%) 9 (1%) Clinic 6 163 (5%) 72 (5%) 166 (6%) 46 (4%) Clinic 7 0 0 4 (0.1%) 1 (0.09%) Clinic 8 174 (5%) 100 (7%) 77 (3%) 0 Clinic 9 192 (6%) 61 (4%) 117 (4%) 10 (1%) Clinic 10 0 0 4 (0.1%) 7 (1%) Clinic 11 231 (7%) 93 (6%) 234 (8%) 17 (1%) Clinic 12 155 (5%) 70 (5%) 116 (4%) 0 Clinic 13 157 (5%) 62 (4%) 150 (5%) 3 (0.3%) Clinic 14 189 (5%) 100 (7%) 139 (5%) 1 (0.1%) Clinic 15 421 (12%) 162 (11%) 258 (9%) 45 (4%) Referral characteristics Medical assistant counseling 3,266 (95%) 1,383 (96%) 2,750 (95%) 1,049 (92%) Provider counseling 2,753 (80%) 1,154 (80%) 2,297 (79%) 843 (74%) Behavioral assistant counseling 814 (24%) 328 (23%) 663 (23%) 82 (7%) Pharmacotherapy 595 (17%) 272 (19%) 519 (18%) 202 (18%) Smoking Cessation Attempts 683 (23%) 323 (26%) 659 (26%) 208 (21%) Asian smokers were more represented among populations with hypertension (16%) and diabetes (22%) and less so among those with depression (10%) or HIV (5%). Latinx smokers were more represented among groups with diabetes (17%), depression (17%), and HIV (18%). Black/African American smokers were well represented in all comorbidity groups, especially among those with hypertension (44%). White smokers were well represented in all groups, especially among those with depression (33%) and HIV (38%). There were high rates of current smokers across comorbidity groups receiving medical assistant (range 92–96%) and provider counseling (74–80%). There was much lower delivery of behavioral assistant counseling (range 7–24%), especially among current smokers with HIV. Notably, those with HIV had consistently the lowest rates of receiving any type of cessation counseling. All comorbidity groups had similar low percentages of receiving cessation pharmacotherapy (17–19%). Cessation rates across comorbidity groups were also similar (21–26%). Discussion Among 51,554 individuals across 15 safety-net primary care clinics, we found 23% were current smokers, and 26% made a recent cessation attempt. Safety-net clinics delivered medical assistant and provider counseling at high rates, though rates were much lower for behavioral assistant counseling and pharmacotherapy. Contrary to our hypothesis, individuals receiving any type of cessation counseling were less likely to make a cessation attempt. This was dissimilar to our prior work, which found higher odds of making a cessation attempt in those who received medical assistant counseling and provider counseling, albeit the prior study was limited to only four clinic sites with a much smaller study sample. 7 Our results highlight how safety-net clinics are able to adequately deliver basic cessation interventions such as provider and medical assistant counseling, and that enhancements in EHR functions allowed demonstration of cessation service delivery. However, delivery of cessation services may not always correlate with cessation attempts, especially in the context of large health systems with diverse subpopulations. For subpopulations with high burden of comorbidities, basic cessation services may be insufficient, highlighting a need for more intensive efforts. 8 , 16 Such efforts may include counseling from care providers of multiple disciplines, combining referrals from different encounters or providers, and streamlined infrastructure to ensure efficient delivery of cessation resources. Several opportunities for interventions among subgroups exist. Although members of Black/African American, Latinx, and Asian communities are less likely to ever smoke and are more likely to be lighter smokers than their White counterparts, they also face disproportionately worse smoking-related health outcomes. 17 – 19 And despite higher interest in quitting than White individuals and past-year quit attempts, Black/African American individuals have lower rates of sustained cessation. These racial disparities can be attributed to structures of systemic racism, including barriers to accessing care, lower receipt of cessation counseling and pharmacotherapy, and increased targeted marketing of tobacco products to racial/ethnic minorities, making sustained cessation more challenging among these communities. 20 – 23 We found communities of color were well represented across comorbidity groups, with Asian smokers well represented among groups with hypertension and diabetes, Latinx smokers represented among those with diabetes, depression, and HIV, and Black/African American represented across all comorbidity groups, especially hypertension. As efforts in addressing diabetes, hypertension, depression, and HIV have all displayed success in improving health outcomes by using a chronic disease management framework, pairing smoking cessation with other chronic disease management efforts may help address racial/ethnic disparities in smoking outcomes. 20 , 24 , 25 Such interventions include telephone or in-person outreach to targeted populations, linking cessation counseling with efforts to improve blood pressure or diabetes care targets, or community engagement practices to inform cessation interventions. Latinx and Non-English speaking patients also had higher odds of recent cessation attempts, highlighting the importance of culturally informed and language concordant cessation counseling and resources. 26 More intensive efforts would align with equity goals to reduce racial disparities across health systems. Our findings demonstrate how EHRs can be an effective tool for identifying smokers and delivering basic smoking cessation services within the context of rapid cycle quality improvement work. In the past decade, financial incentive programs for meaningful use of EHRs have increased tobacco screening, documentation of smoking status, and delivery of cessation services in safety-net settings. 11,27−29 Additionally, the EHR has shown to be effective in rapidly identifying factors associated with cessation attempts and the receipt of referral services, which could be used to drive quality improvement activities to improve health outcomes. 7 , 13 In the face of many competing priorities, health systems are required to meet minimum criteria to obtain reimbursement and incentives from public insurers. For example, The Public Hospital Redesign and Incentives in Medi-Cal program (PRIME) requires evidence-based quality improvement goals for clinics, including screening for smoking status and counseling every two years. 30 However best practices guidelines recommend assessments at every clinical encounter to optimize chances of cessation, 31 highlighting how more intensive interventions than those required by public insurers may be needed to improve patient outcomes. Therefore, clinics should acknowledge the need to meet minimum requirements for reimbursement, but also to take measures to strive for best practice recommendations. Health systems can do so by streamlining efforts, including assigning responsibilities to each health team member for providing smoking cessation services or providing guidance on how frequently these services should be provided to avoid redundancy and waste of resources. The EHR is also advantageous in identifying populations that need these intensive interventions, and the PRECEDE-PROCEED model can be used to develop interventions in these contexts. For example, the SFHN implemented an EPIC Enterprise EHR in August 2020. Our evaluation using the PRECEDE-PROCEED is therefore timely in providing critical information to developing system-level approaches to support cessation efforts throughout the network. Already, efforts from this work have led to creation of a tobacco registry that includes a better screening tool for tobacco use embedded within the new EHR and templates to document counseling interventions. The registry can be used to track receipt of cessation services and drive practice changes in delivery of cessation care. In our study, we found that only a quarter of current smokers made a recent cessation attempt, and of those who made a smoking cessation attempt at visit 2, about half of them relapsed by visit 3. These rates of cessation attempts were lower than the estimated 44% of smoking cessation attempts in the past year made by the general US population. 32 Little is known about the rates of cessation attempts in primary care settings, with few studies estimating roughly 36–39% of patients making a recent cessation attempt and 15–20% of patients maintain cessation at one year. 24 , 25 , 33 , 34 Ours was one of the few studies evaluating cessation attempts and relapse rates at clinic or system levels within a safety-net system. Because most smokers who attempt cessation are likely to relapse with high average lifetime number of quit attempts before sustained cessation, 35 , 36 program initiatives should pay increasing attention toward sustaining cessation attempts and streamlining interventions to determine which groups require more intensive efforts. There are several limitations to our study. EHR data relied on patient self-report, and smoking status was not biochemically verified, leading to a potential misclassification bias. However, our repeated assessments of smoking status over time may have reduced potential misclassifications. By excluding people with missing smoking status in the analysis, we may have also introduced some bias. 7 Still, our large sample size may have protected against this and allows our data to be generalizable, as the inclusion of a diverse array of patients and clinics may be representative of other safety-net settings. The quality of smoking status data collection could have varied across clinic sites, though all clinics had the same EHR with a structured format for data collection. 28 Finally, for some clinics especially those serving young adult populations, the actual numbers of patients who attempted recent cessation were small, leading to inflated percentages of relapse in these clinics. In conclusion, the EHR can be used to efficiently understand and identify opportunities for improvement in delivering smoking cessation services, especially in subpopulations that may require more intensive, directed efforts to achieve sustained cessation. 37 – 39 Safety-net providers and clinic leaders could consider using the EHR to enhance the reach and efficacy of smoking cessation services, and target subpopulations with high needs in order to reduce racial and health disparities in safety-net settings. Declarations Ethics approval and consent to participate: This study was approved by the University of California, San Francisco Committee on Human Research (#18-26398). Consent for publication: Not applicable. Competing interests: The authors declare that they have no competing interests. Funding: This study was supported by the National Heart, Lung and Blood Institute (R38 HL143581) and the Tobacco-Related Disease Research Program (28CP-0038). The funding agencies had no role in study design, data collection, analysis, the decision to publish, or the preparation of the manuscript. Data availability: The datasets generated during and/or analyzed during the current study are not publicly available due to ongoing data collection and analysis but are available from the corresponding author on reasonable request. Authors contributions: V., H.R., and E.C. conceptualized the design of the study. T.L. performed data analysis with guidance from M.V. and L.S. L.S. and M.V. wrote the manuscript with support from K.C., E.S., T.L., E.C., and H.R. All authors contributed to the final version of the manuscript. Acknowledgements: Not applicable. Prior presentations : This work was presented as a poster presentation at the San Francisco Bay Area Collaborative Research Network 2020 Annual Meeting. References United States Surgeon General. The Health Consequences of Smoking -- 50 Years of progress: A Report of the Surgeon General: (510072014-001). Published online 2014. doi:10.1037/e510072014-001 Jamal A, Phillips E, Gentzke AS. Current Cigarette Smoking Among Adults — United States, 2016. MMWR Morb Mortal Wkly Rep . 2018;67:53-59. doi:10.15585/mmwr.mm6702a1 Weinberger AH, Gbedemah M, Wall MM, Hasin DS, Zvolensky MJ, Goodwin RD. Cigarette use is increasing among people with illicit substance use disorders in the United States, 2002-14: emerging disparities in vulnerable populations. Addiction . 2018;113(4):719-728. doi:10.1111/add.14082 Stead LF, Perera R, Bullen C, et al. Nicotine replacement therapy for smoking cessation. Cochrane Database Syst Rev . 2012;11:CD000146. doi:10.1002/14651858.CD000146.pub4 Stead LF, Buitrago D, Preciado N, Sanchez G, Hartmann-Boyce J, Lancaster T. Physician advice for smoking cessation. Cochrane Database Syst Rev . 2013;(5):CD000165. doi:10.1002/14651858.CD000165.pub4 Park ER, Gareen IF, Japuntich S, et al. Primary Care Provider-Delivered Smoking Cessation Interventions and Smoking Cessation Among Participants in the National Lung Screening Trial. JAMA Intern Med . 2015;175(9):1509-1516. doi:10.1001/jamainternmed.2015.2391 Gubner NR, Williams DD, Chen E, et al. Recent cessation attempts and receipt of cessation services among a diverse primary care population - A mixed methods study. Prev Med Rep . 2019;15:100907. doi:10.1016/j.pmedr.2019.100907 Bernstein SL, Weiss J, DeWitt M, et al. A randomized trial of decision support for tobacco dependence treatment in an inpatient electronic medical record: clinical results. Implement Sci . 2019;14(1):8. doi:10.1186/s13012-019-0856-8 Blumenthal D, Tavenner M. The “meaningful use” regulation for electronic health records. N Engl J Med . 2010;363(6):501-504. doi:10.1056/NEJMp1006114 Centers for Medicare & Medicaid Services (CMS), HHS. Medicare and Medicaid programs; electronic health record incentive program. Final rule. Fed Regist . 2010;75(144):44313-44588. Bailey SR, Heintzman JD, Marino M, et al. Smoking-Cessation Assistance: Before and After Stage 1 Meaningful Use Implementation. Am J Prev Med . 2017;53(2):192-200. doi:10.1016/j.amepre.2017.02.006 Herbst N, Wiener RS, Helm ED, et al. Effectiveness of an Opt-Out Electronic-Heath Record-based Tobacco Treatment Consult Service at an Urban Safety-Net Hospital. Chest . Published online May 16, 2020. doi:10.1016/j.chest.2020.04.062 Fiore M, Adsit R, Zehner M, et al. An electronic health record–based interoperable eReferral system to enhance smoking Quitline treatment in primary care. J Am Med Inform Assoc . 2019;26(8-9):778-786. doi:10.1093/jamia/ocz044 Zhu S-H, Anderson CM, Tedeschi GJ, et al. Evidence of Real-World Effectiveness of a Telephone Quitline for Smokers. New England Journal of Medicine . 2002;347(14):1087-1093. doi:10.1056/NEJMsa020660 Green LW, Kreuter MW. Health Program Planning: An Educational and Ecological Approach . 4th edition. McGraw-Hill; 2005. Accessed June 24, 2020. https://www.researchgate.net/publication/301749054_Green_LW_Kreuter_MW_Health_Program_Planning_An_Educational_and_Ecological_Approach_4th_Edition_New_York_McGraw-Hill_2005 Jose T, Ohde JW, Hays JT, Burke MV, Warner DO. Design and Pilot Implementation of an Electronic Health Record-Based System to Automatically Refer Cancer Patients to Tobacco Use Treatment. Int J Environ Res Public Health . 2020;17(11). doi:10.3390/ijerph17114054 Trinidad DR, Pérez-Stable EJ, White MM, Emery SL, Messer K. A Nationwide Analysis of US Racial/Ethnic Disparities in Smoking Behaviors, Smoking Cessation, and Cessation-Related Factors. Am J Public Health . 2011;101(4):699-706. doi:10.2105/AJPH.2010.191668 Trinidad DR, Pérez-Stable EJ, Emery SL, White MM, Grana RA, Messer KS. Intermittent and light daily smoking across racial/ethnic groups in the United States. Nicotine & Tobacco Research . 2009;11(2):203-210. doi:10.1093/ntr/ntn018 Sakuma K-LK, Felicitas-Perkins JQ, Blanco L, et al. Tobacco use disparities by racial/ethnic groups: California compared to the United States. Preventive Medicine . 2016;91:224-232. doi:10.1016/j.ypmed.2016.08.035 Babb S. Quitting Smoking Among Adults — United States, 2000–2015. MMWR Morb Mortal Wkly Rep . 2017;65. doi:10.15585/mmwr.mm6552a1 CDCTobaccoFree. African Americans and Tobacco Use. Centers for Disease Control and Prevention. Published November 18, 2019. Accessed August 12, 2020. https://www.cdc.gov/tobacco/disparities/african-americans/index.htm CDC. Hispanics/Latinos and Tobacco Use. Centers for Disease Control and Prevention. Published December 17, 2018. Accessed August 12, 2020. https://www.cdc.gov/tobacco/disparities/hispanics-latinos/index.htm Bailey SR, Heintzman J, Jacob RL, Puro J, Marino M. Disparities in Smoking Cessation Assistance in US Primary Care Clinics. Am J Public Health . 2018;108(8):1082-1090. doi:10.2105/AJPH.2018.304492 Bailey SR, Stevens VJ, Fortmann SP, et al. Long-Term Outcomes From Repeated Smoking Cessation Assistance in Routine Primary Care: American Journal of Health Promotion . Published online March 13, 2018. doi:10.1177/0890117118761886 Stevens VJ, Solberg LI, Bailey SR, et al. Assessing Trends in Tobacco Cessation in Diverse Patient Populations. Nicotine Tob Res . 2016;18(3):275-280. doi:10.1093/ntr/ntv092 Parker MM, Fernández A, Moffet HH, Grant RW, Torreblanca A, Karter AJ. Association of Patient-Physician Language Concordance and Glycemic Control for Limited-English Proficiency Latinos With Type 2 Diabetes. JAMA Intern Med . 2017;177(3):380-387. doi:10.1001/jamainternmed.2016.8648 Kruse G, Chang Y, Kelley JH, Einbinder J, Rigotti NA. Healthcare System Effects of Pay-for-performance for Smoking Status Documentation. Published online 2014:15. Polubriaginof F, Salmasian H, Albert DA, Vawdrey DK. Challenges with Collecting Smoking Status in Electronic Health Records. AMIA Annu Symp Proc . 2018;2017:1392-1400. Accessed June 23, 2020. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5977725/ Vidrine JI, Shete S, Li Y, et al. The Ask–Advise–Connect Approach for Smokers in a Safety Net Healthcare System: A Group-Randomized Trial. American Journal of Preventive Medicine . 2013;45(6):737-741. doi:10.1016/j.amepre.2013.07.011 Kaslow AA, Romano PS, Schwarz E, Shaikh U, Tong EK. Building and Scaling-up California Quits: Supporting Health Systems Change for Tobacco Treatment. American Journal of Preventive Medicine . 2018;55(6):S214-S221. doi:10.1016/j.amepre.2018.07.045 Fiore MC, Jaen CR, Baker TB, et al. Treating Tobacco Use and Dependence: 2008 Update . US Department of Health and Human Services. Public Health Service.; 2008. Accessed August 12, 2020. https://www.ncbi.nlm.nih.gov/books/NBK63952/ Kahende JW, Malarcher AM, Teplinskaya A, Asman KJ. Quit Attempt Correlates among Smokers by Race/Ethnicity. Int J Environ Res Public Health . 2011;8(10):3871-3888. doi:10.3390/ijerph8103871 Wadland WC, Stöffelmayr B, Berger E, Crombach A, Ives K. Enhancing smoking cessation rates in primary care. J Fam Pract . 1999;48(9):711-718. Bold KW, Rasheed AS, McCarthy DE, Jackson TC, Fiore MC, Baker TB. Rates and Predictors of Renewed Quitting After Relapse During a One-Year Follow-Up Among Primary Care Patients. Ann Behav Med . 2015;49(1):128-140. doi:10.1007/s12160-014-9627-6 García-Rodríguez O, Secades-Villa R, Flórez-Salamanca L, Okuda M, Liu S-M, Blanco C. Probability and predictors of relapse to smoking: Results of the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC). Drug Alcohol Depend . 2013;132(3):479-485. doi:10.1016/j.drugalcdep.2013.03.008 Chaiton M, Diemert L, Cohen JE, et al. Estimating the number of quit attempts it takes to quit smoking successfully in a longitudinal cohort of smokers. BMJ Open . 2016;6(6). doi:10.1136/bmjopen-2016-011045 Auerbach A, Bates DW. Introduction: Improvement and Measurement in the Era of Electronic Health Records. Annals of Internal Medicine . 2020;172(11_Supplement):S69-S72. doi:10.7326/M19-0870 Kawamoto K, McDonald CJ. Designing, Conducting, and Reporting Clinical Decision Support Studies: Recommendations and Call to Action. Ann Intern Med . 2020;172(11_Supplement):S101-S109. doi:10.7326/M19-0875 Zheng K, Ratwani RM, Adler-Milstein J. Studying Workflow and Workarounds in Electronic Health Record-Supported Work to Improve Health System Performance. Ann Intern Med . 2020;172(11_Supplement):S116-S122. doi:10.7326/M19-0871 Appendix Appendix Table 1. Demographics by type of clinics Academic clinics (N = 21314) Community clinics (N = 30240) Age 50.2 (16.5) 52.8 (15.5) Gender Male 10347 (49%) 14300 (47%) Female 10967 (51%) 15940 (53%) Race/ethnicity Hispanic 8701 (41%) 6388 (21%) American Indian or Alaska Native 164 (1%) 144 (0.5%) Asian 4337 (21%) 10686 (36%) Black/African American 2558 (12%) 5133 (17%) Native Hawaiian or other Pacific Island 171 (1%) 338 (1%) White 3358 (16%) 6029 (20%) Other 1740 (8%) 1057 (4%) Language English 12359 (58%) 17406 (58%) Spanish 6200 (29%) 4360 (14%) Cantonese/Mandarin/Chinese 1374 (7%) 6804 (23%) Vietnamese 448 (2%) 271 (1%) Russian 192 (1%) 519 (2%) Tagalog 205 (1%) 192 (1%) Korean 18 (0.1%) 221 (1%) Arabic 94 (0.4%) 51 (0.2%) Other 263 (1%) 300 (1%) Insurance Healthy San Francisco 1910 (10%) 1809 (7%) Medi-Cal 10262 (54%) 12677 (47%) Medicare 3784 (20%) 5417 (20%) Private 304 (2%) 295 (1%) Other coverage 2092 (11%) 6094 (23%) Uninsured 551 (3%) 624 (2%) Recent Smoking Cessation Attempt* 891 (33%) 1017 (22%) *For recent cessation attempts, only individuals with at least three primary care encounters with smoking status were included in this measure, leading to N = 2664 for academic clinics and N = 4724 for community clinics. Appendix Table 2. International Classification of Diseases 9 or 10 Diagnoses Extracted to Characterize Presence of Comorbidities Diagnosis ICD9 code ICD10 code Asthma 493 J45 Chronic Obstructive Pulmonary Disease 491–494, 496 J41-44, J47 Depression 290, 296, 298, 300, 301, 309, 311 F01, F32-F34, F43 Diabetes 250, 357, 362, 366, 648 E10, E11, E13, Q24 HIV 042, V08 B20, Z21 Hyperlipidemia E78 Hypertension 401–404 I10-13 Ischemic vascular disease 411, 413, 414, 429, 433, 434, 437, 440, 444, 445 I20, I24, I25, I63, I65-67, I70, I75, T82 Heart failure 398, 402, 404, 428 I09, I11, I13, I50 Kidney disease A18, A52, B52, C64, C68, D30, D41, D59, E08-E11, E13, E74, I12, I13, I70, I72, K76, M10, M32, M35, N00-08, N13-19, N25, N26, Q61, Q62, R94 Appendix Table 3. The relapse rate at visit 3 among smokers who made recent quit attempts in visit 2 n (%) Overall 536 (45%) By clinic Clinic 1 62 (50%) Clinic 2 3 (50%) Clinic 3 105 (45%) Clinic 4 37 (45%) Clinic 5 112 (50%) Clinic 6 31 (33%) Clinic 7 1 (50%) Clinic 8 22 (50%) Clinic 9 10 (37%) Clinic 10 0 (0%) Clinic 11 32 (45%) Clinic 12 29 (47%) Clinic 13 21 (48%) Clinic 14 30 (45%) Clinic 15 41 (39%) Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Major revision 18 Mar, 2021 Review # 3 received at journal 11 Mar, 2021 Reviewer # 3 agreed at journal 03 Mar, 2021 Review # 1 received at journal 28 Feb, 2021 Review # 2 received at journal 27 Feb, 2021 Reviewer # 2 agreed at journal 08 Feb, 2021 Reviewer # 1 agreed at journal 08 Feb, 2021 Reviewers invited by journal 27 Dec, 2020 Editor assigned by journal 21 Nov, 2020 Submission checks completed at journal 21 Nov, 2020 Editor invited by journal 21 Nov, 2020 First submitted to journal 10 Nov, 2020 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-115150","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research article","associatedPublications":[],"authors":[{"id":5271408,"identity":"395d7eaa-3e09-4ec1-9019-104b6df58396","order_by":0,"name":"Leslie W. Suen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6ElEQVRIie2QsQrCMBCGrxTaJbXrOfkKEaE4CH2VZnHKJoigSEDQyQcQfAhdnAOFdgnOBQfr5NLB0UmMqOCUdnTIB3fccB/8dwAWyx/SAnBk8p6dUrdQFzUqnq6v4r5W26KR8p2xmeKvSllOIKb5OpuS2RzBXxzQqBBFZaKA7dRxeCJZikCysVlBDpItIaEFj06BkHNAHpmVzrWU7KGDnatoFAgdrFPVKAhUMgHOriCRGwgXAUmNQri+JUO2UbzX3upbPDIc9U1K6OeXy302iFu56t4q/bHQT/eFSfnwk8RrsG6xWCyWGp6qk0NkEJ3uJwAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0003-4786-356X","institution":"National Clinician Scholars Program, Philip R. Lee Institute of Health Policy Studies, University of California San Francisco, CA, USA","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Leslie","middleName":"W.","lastName":"Suen","suffix":""},{"id":5271409,"identity":"a6254fca-efa8-4be1-8b63-075dcec861cf","order_by":1,"name":"Henry Rafferty","email":"","orcid":"","institution":"San Francisco Department of Public Health","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Henry","middleName":"","lastName":"Rafferty","suffix":""},{"id":5271410,"identity":"2e41f57b-a553-4a3b-ac7b-33e53114b699","order_by":2,"name":"Thao Le","email":"","orcid":"","institution":"UCSF: University of California San Francisco","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Thao","middleName":"","lastName":"Le","suffix":""},{"id":5271411,"identity":"4274b109-109c-4566-84c4-ff121295047b","order_by":3,"name":"Kara Chung","email":"","orcid":"","institution":"San Francisco Department of Public Health","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Kara","middleName":"","lastName":"Chung","suffix":""},{"id":5271412,"identity":"996c7efc-8ee4-4df1-bad1-466dd7bdf3dc","order_by":4,"name":"Elana Straus","email":"","orcid":"","institution":"UCSF: University of California San Francisco","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Elana","middleName":"","lastName":"Straus","suffix":""},{"id":5271413,"identity":"958413de-87f4-4ab3-bacb-74305236a0d1","order_by":5,"name":"Ellen Chen","email":"","orcid":"","institution":"San Francisco Department of Public Health","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ellen","middleName":"","lastName":"Chen","suffix":""},{"id":5271414,"identity":"5c0cc8d6-aa83-4bf9-a689-ea7394a4e575","order_by":6,"name":"Maya Vijayaraghavan","email":"","orcid":"","institution":"UCSF: University of California San Francisco","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Maya","middleName":"","lastName":"Vijayaraghavan","suffix":""}],"badges":[],"createdAt":"2020-11-24 15:20:47","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-115150/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-115150/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":3925603,"identity":"48a580e5-bc5b-4065-90bb-58f49abfcf09","added_by":"auto","created_at":"2020-12-01 17:29:34","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":27937,"visible":true,"origin":"","legend":"Cumulative rate of a recent cessation attempt across San Francisco Health Network clinics from May 2016 to May 2019 (N= 7,388)","description":"","filename":"Figure1.JPG","url":"https://assets-eu.researchsquare.com/files/rs-115150/v1/0bdded1086a9639892af2d04.JPG"},{"id":13621093,"identity":"2a2096b4-2e1f-41ea-882a-674775b2c67b","added_by":"auto","created_at":"2021-09-17 07:08:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":631603,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-115150/v1/ff149be1-7253-4557-bfad-066f42e3de41.pdf"}],"financialInterests":"","formattedTitle":"\u003cp\u003eDelivery of Smoking Cessation Services and Cessation Attempts Across a Public, Safety-Net Primary Care System\u003c/p\u003e","fulltext":[{"header":"Introduction","content":" \u003cp\u003eDespite decades of efforts to reduce tobacco use in the United States (US), cigarette smoking accounts for one in five deaths annually and remains a leading cause of preventable death.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e The prevalence of smoking and burden of tobacco-caused diseases further remain disproportionately high in individuals with low socioeconomic status, mental illness, substance use disorders, and communities of color.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e Safety-net health systems that provide care for populations with high smoking rates are therefore uniquely poised to provide smoking cessation services, though many competing priorities and limited resources make providing cessation care challenging.\u003c/p\u003e \u003cp\u003eOur prior work, using a PRECEDE-PROCEED evaluation framework, explored enabling factors and barriers to delivery of cessation services within a subset of safety-net clinics.\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e We found that due to competing priorities, clinic staff trained in offering cessation counseling (e.g., behavioral assistants) were not able to meet the demands of \u0026ldquo;warm hand offs\u0026rdquo; for counseling during encounters, requiring rescheduling visits that resulted in low follow up. Lack of both coordination of cessation services and ownership of counseling by clinical workforces also posed barriers to successful delivery of cessation services.\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e Less is known about patient-level factors associated with smoking cessation attempts and receipt of cessation services within safety-net populations who have comorbid chronic physical and mental health conditions.\u003c/p\u003e \u003cp\u003eThis study expands on prior work by using the electronic health record (EHR) to identify factors associated with smoking cessation attempts among all primary care clinics in the San Francisco Health Network (SFHN), the largest network of primary care clinics, specialty clinics, acute care hospitals, and behavioral health clinics serving publicly insured and uninsured patients in San Francisco. In recent years, the SFHN has undertaken various quality improvement initiatives to improve smoking cessation care across the entire health system. Understanding patient-level characteristics and factors associated with cessation attempts can enable opportunities to develop future interventions within SFHN and other urban safety-net systems. We hypothesized that delivery of cessation services, including any counseling from medical assistants, providers, and behavioral assistants, as well as pharmacotherapy, would be associated with cessation attempts.\u003c/p\u003e "},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n\u003ch2\u003eStudy setting and population\u003c/h2\u003e\n\u003cp\u003eWe extracted patient-level EHR data on smoking status and delivery of smoking cessation services from 15 primary care clinics across SFHN (Appendix 1). Four of the 15 clinics (Clinics 2\u0026ndash;6) are academic primary care practices housed within a university-affiliated public hospital, with three general primary care clinics and one clinic focusing on care for patients living with HIV. The remaining 11 clinics are community-based clinics dispersed across San Francisco with varying patient demographics. Clinic 1 is located in the downtown area and provides care predominantly to people experiencing homelessness, whereas other clinics serve a higher proportion of Black, Latinx, Asian, and other racial/ethnic communities.\u003c/p\u003e\n\u003cp\u003eAt each clinic visit, medical assistants (i.e., frontline medical staff) screen patients for smoking status as \u0026ldquo;current smoker,\u0026rdquo; \u0026ldquo;past smoker,\u0026rdquo; or \u0026ldquo;never smoker\u0026rdquo; and input their status into the EHR. To be consistent with prior literature and clinical practice, we refer to \u0026ldquo;past smokers\u0026rdquo; as \u0026ldquo;former smokers\u0026rdquo; for the remainder of the study.\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e For current smokers, medical assistants can refer to one or more cessation resources including those specific to the clinic (e.g., on-site smoking cessation groups or counseling by the behavioral assistants) and/or general resources (e.g., the California Smoker\u0026rsquo;s Helpline).\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e Providers and medical assistants during the encounter may also refer to behavioral assistants for counseling. Behavioral assistants are ancillary staff trained to offer smoking cessation coaching, and they offer on-site or telephone counseling to patients. Patients can also receive counseling from their provider and/or initiate smoking cessation pharmacotherapy through a prescription from their provider or a pharmacist during the clinical encounter. This study was approved by the University of California, San Francisco Committee on Human Research (#18-26398).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n\u003ch2\u003eStudy Design\u003c/h2\u003e\n\u003cp\u003eThe study utilized the PRECEDE-PROCEED model as its theoretical framework, an implementation science model used for assessing the health needs within a public health program, and for designing, implementing, and evaluating programs to meet these needs.\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e In our prior work, we conducted social, epidemiological, and ecological assessments to identify predisposing and reinforcing factors (PRECEDE) to increase delivery of cessation services.\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e We found that provision of medical services using team-based care models is feasible, and that the EHR could be a useful tool to facilitate delivery of cessation care. Here, we explore potential Policy, Regulatory, and Organizational (PROCEED) factors in order to facilitate the implementation of system-level interventions to increase delivery of cessation services and evaluation of such interventions.\u003c/p\u003e\n\u003cp\u003eWe extracted data from the EHR on any patient of the 15 clinic sites who had at least one recorded smoking status and at least three unique primary care encounters between May 2016 to May 2019. The index visit was the first encounter closest to the study\u0026rsquo;s start date. Current smokers reported smoking at the index visit, whereas non-smokers were either former smokers or never smokers. We extracted data from the EHR using i2i Tracks (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.i2isys.com/p/i2itracks\u003c/span\u003e\u003c/span\u003e) software and Structured Query Language (SQL).\u003c/p\u003e\n\u003cp\u003eWe extracted patient level factors including age, gender, primary language, race/ethnicity, health insurance type, and smoking-related comorbidities from the EHR. Health insurance was categorized as either Medicare, Medicaid (named \u0026ldquo;Medi-Cal\u0026rdquo; in California), Healthy San Francisco/Healthy Workers (a county-based program providing medical care to low-income adults), private, other coverage, or uninsured. Smoking-related health comorbidities were identified using ICD-9 and ICD-10 codes listed as diagnoses within the patient\u0026rsquo;s medical record (Appendix 2).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n\u003ch2\u003eData Analysis\u003c/h2\u003e\n\u003cp\u003eWe used descriptive statistics to summarize the data, with frequencies and percentages for categorical variables and means with standard deviations (SD) for continuous variables. We compared current smokers and non-smokers based on smoking status at the index visit. We used Pearson\u0026rsquo;s chi-square test for categorical variables and the t-test for continuous variables when comparing demographic characteristics, health insurance type, comorbidities, clinic, and referral characteristics.\u003c/p\u003e\n\u003cp\u003eWe evaluated factors associated with making a recent smoking cessation attempt. Recent smoking cessation attempt was defined as a transition in smoking status from current smoker at the index to former smoker either at visit 2 or 3. We chose independent variables previously shown to be associated with smoking cessation attempts, including demographics, health insurance type, clinic, visit number, comorbidities, and receipt of cessation services including counseling (medical assistant counseling, provider counseling, behavioral assistant counseling) and pharmacotherapy.\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e We examined the multivariable association between recent cessation attempts and covariates using generalized estimating equations (GEE), accounting for nesting patients within clinics with an exchangeable correlation structure. We further characterized rates of recent cessation attempts as well as the relapse rates by clinic. We defined a relapse as a change in smoking status from former smoker at visit 2 to current smoker at visit 3.\u003c/p\u003e\n\u003cp\u003eTo better understand disparities in delivery of smoking cessation services and delineate which subpopulations may benefit from more intensive efforts, we described characteristics within patient groups with various comorbidities (\u003cem\u003ei.e.\u003c/em\u003e, hypertension, diabetes, depression, and/or HIV), including their demographics, health insurance type, clinic, receipts of smoking cessation counseling and pharmacotherapy, and cessation attempts. All analyses were completed using SAS version 9.4.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n\u003ch2\u003eIndividual Characteristics among Smokers and Non-Smokers\u003c/h2\u003e\n\u003cp\u003eThe sample included 51,554 individuals from 15 clinics, of whom 11,622 (23%) were current smokers (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Approximately 37% of current smokers and 43% of non-smokers came from the four academic, hospital-based clinics. Compared to non-smokers, current smokers were more likely to be younger (mean age 50.3\u0026nbsp;years +/- SD 13.9 vs 52.2\u0026nbsp;years +/- SD 16.5), male (68% vs 42%), and White (27% vs 16%) or Black/African American (32% vs 10%). Current smokers were also more likely to be English-speaking (81% vs 51%) and had higher prevalence of asthma (10% vs 8%), COPD (14% vs 4%), depression (32% vs 25%), HIV (12% vs 4%), and heart failure (4% vs 3%).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eDemographic, Comorbidities and Referral Characteristics among Smokers and Non-smokers in San Francisco Health Network clinics (N\u0026thinsp;=\u0026thinsp;51,203)\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eMean (SD) or n (%)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003ep-value\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eCurrent smokers\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(N\u0026thinsp;=\u0026thinsp;11,622)\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eNon-smokers \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(N\u0026thinsp;=\u0026thinsp;39,581)\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e50.3 (13.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e52.2 (16.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eGender\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7,921 (68.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16,516 (42%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFemale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3,701 (32%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e23,065 (58%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eRace/ethnicity\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHispanic\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2,034 (18%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12,965 (33%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAmerican Indian/Alaska Native\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e119 (1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e187 (0.5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAsian\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1,859 (16%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13,119 (34%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBlack/African American\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3,622 (32%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3,973 (10%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNative Hawaiian/Pacific Islander\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e150 (1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e352 (0.9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWhite\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3,096 (27%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6,236 (16%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOther\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e564 (5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2,184 (6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eLanguage\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEnglish\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9,340 (81%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e20,179 (51%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSpanish\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e958 (8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9,526 (24%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCantonese/Mandarin/Chinese\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e884 (8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7,283 (18%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eVietnamese\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e80 (0.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e634 (2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRussian\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e125 (1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e585 (1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTagalog\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e43 (0.4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e353 (0.9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eKorean\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e24 (0.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e213 (0.5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eArabic\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e24 (0.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e121 (0.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOther\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e58 (0.5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e499 (1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eInsurance\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHealthy San Francisco\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e499 (5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3,198 (9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMedi-Cal\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6,177 (59%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16,552 (47%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMedicare\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2,101 (20%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7,060 (20%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePrivate\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e112 (1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e479 (1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOther coverage\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1,190 (11%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6,967 (20%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUninsured\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e335 (3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e816 (2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eComorbidities\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAsthma\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1,102 (9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3,126 (8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eChronic obstructive lung disease\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1,567 (13%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1,572 (4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDepression\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3,737 (32%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9,924 (25%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDiabetes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1,863 (16%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8,341 (21%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHIV\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1,424 (12%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1,715 (4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHyperlipidemia\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2,448 (21%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11,843 (30%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHypertension\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4,309 (37%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15,197 (38%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.01\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIschemic heart disease\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e627 (5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2,206 (6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.46\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHeart failure\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e491 (4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1,286 (3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eKidney disease\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1,114 (10%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4,242 (11%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eClinic\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eClinic 1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1,949 (17%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1,512 (4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eClinic 2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e52 (0.5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e413 (1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eClinic 3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1,562 (13%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8,385 (21%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eClinic 4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1,006 (9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1,281 (3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eClinic 5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1,637 (14%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6,892 (17%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eClinic 6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e712 (6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2,910 (7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eClinic 7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e28 (0.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14 (0.04%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eClinic 8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e645 (6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4,413 (11%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eClinic 9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e380 (3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e764 (2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eClinic 10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e24 (0.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12 (0.03%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eClinic 11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e732 (6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2,570 (6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eClinic 12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e592 (5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3,244 (8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eClinic 13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e487 (4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1,531 (4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eClinic 14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e634 (5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3,489 (9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eClinic 15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1,182 (10%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2,149 (5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eReferral characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMedical assistant counseling\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10,426 (90%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eProvider counseling\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8,467 (73%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBehavioral assistant counseling\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2,031 (17%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePharmacotherapy\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1,419 (12%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eLogistic regression model of factors associated with making a recent smoking cessation attempt among Smokers in San Francisco Health Network clinics\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eUnadjusted OR\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eAdjusted OR\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eOR (95%CI)\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ep value\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eAOR (95%CI)\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ep value\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.98 (0.97, 0.98)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.99 (0.98, 0.99)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eGender\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMale (ref.)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFemale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.21 (1.09, 1.34)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.25 (1.10, 1.41)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eRace/ethnicity\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWhite (ref.)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAmerican Indian/Alaska Native\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.30 (0.83, 2.05)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.82 (1.10, 3.02)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.02\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAsian\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.88 (0.74, 1.03)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.02 (0.82, 1.28)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.85\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBlack/African American\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.00 (0.87, 1.14)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.99\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.03 (0.88, 1.21)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.70\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHispanic\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.90 (1.64, 2.20)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.31 (1.08, 1.60)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.006\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNative Hawaiian/Pacific Islander\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.28 (0.83, 1.98)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.27\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.98 (0.59, 1.65)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.95\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOther\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.50 (1.18, 1.91)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.05 (0.80, 1.38)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.71\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eLanguage\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEnglish (ref.)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eArabic\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.97 (0.71, 5.44)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.29 (0.41, 4.03)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.66\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCantonese/Mandarin/Chinese\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.53 (0.42, 0.66)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.72 (0.52, 1.01)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.06\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eKorean\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.20 (0.03, 1.50)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.27 (0.03, 2.21)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.22\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOther\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.11 (0.28, 1.60)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.36\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.67 (0.28, 1.60)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.36\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRussian\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.98 (0.55, 2.23)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.78\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.98 (0.55, 1.76)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.95\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSpanish\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.30 (1.96, 2.69)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.44 (1.15, 1.81)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTagalog\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.66 (0.24, 1.85)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.43\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.86 (0.24, 3.10)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.82\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eVietnamese\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.00 (0.58, 1.72)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.99\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.78 (0.43, 1.43)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.42\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eInsurance type\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMedicare (ref.)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHealthy San Francisco\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.18 (1.71, 2.79)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.81 (0.60, 1.09)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.16\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMedi-Cal\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.28 (1.12, 1.46)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.86 (0.73, 1.01)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.06\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePrivate\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.23 (0.72, 2.13)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.45\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.62 (0.33, 1.17)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.14\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOther\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.02 (0.84, 1.24)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.86\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.87 (0.69, 1.09)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.22\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUninsured\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.08 (0.77, 1.52)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.65\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.75 (0.52, 1.09)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.13\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eClinic\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eClinic 1 (ref.)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eClinic 2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.52 (3.13, 23.19)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.82 (1.58, 14.73)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.006\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eClinic 3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.78 (2.31, 3.34)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.43 (1.96, 3.02)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eClinic 4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.38 (1.11, 1.73)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.005\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.14 (0.79, 1.63)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.49\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eClinic 5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.85 (2.37, 3.42)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.58 (2.08, 3.20)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eClinic 6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.55 (2.01, 3.22)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.90 (2.22, 3.77)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eClinic 7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.87 (0.65, 1.16)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.33\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.57 (1.08, 2.28)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.02\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eClinic 8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.89 (0.20, 4.02)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.88\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.64 (0.12, 3.35)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.59\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eClinic 9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.88 (0.61, 1.25)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.47\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.19 (0.78, 1.81)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.41\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eClinic 10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.29 (0.44, 3.78)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.64\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.25 (0.43, 3.66)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.68\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eClinic 11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.63 (1.28, 2.07)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.03 (1.55, 2.65)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eClinic 12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.57 (1.21, 2.05)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.38 (1.74, 3.26)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eClinic 13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.61 (1.20, 2.16)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.76 (1.27, 2.43)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eClinic 14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.02 (1.57, 2.61)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.89 (1.41, 2.54)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eClinic 15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.48 (1.19, 1.85)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.30 (1.01, 1.68)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.04\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eVisit\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eVisit 2 (ref.)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eVisit 3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.69 (0.62, 0.76)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.77 (0.69, 0.86)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eComorbidities\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAsthma\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.99 (0.84, 1.17)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.91\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.03 (0.85, 1.24)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.78\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eChronic obstructive lung disease\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.68 (0.58, 0.78)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.02 (0.85, 1.23)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.82\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDepression\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.02 (0.92, 1.13)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.70\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.18 (1.05, 1.33)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.006\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDiabetes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.98 (0.86, 1.12)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.75\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.00 (0.85, 1.18)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.97\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHIV\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.76 (0.65, 0.89)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.21 (0.90, 1.64)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.21\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHyperlipidemia\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.85 (0.75, 0.96)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.007\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.17 (1.00, 1.36)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.05\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHypertension\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.77 (0.70, 0.86)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.03 (0.90, 1.18)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.62\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIschemic heart disease\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.00 (0.81, 1.23)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.98\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.36 (1.06, 1.74)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.02\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHeart failure\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.03 (0.82, 1.30)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.79\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.26 (0.96, 1.64)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.10\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eKidney disease\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.90 (0.77, 1.06)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.22\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.06 (0.87, 1.29)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.58\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eCounseling and pharmacotherapy\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMedical assistant counseling\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.36 (0.32, 0.40)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.35 (0.31, 0.40)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eProvider counseling\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.44 (0.40, 0.49)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.66 (0.58, 0.76)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBehavioral assistant counseling\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.63 (0.55, 0.72)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.83 (0.71, 0.98)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.03\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePharmacotherapy\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.72 (0.62, 0.83)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.85 (0.71, 1.02)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.08\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n\u003ch2\u003eReceipt of counseling and pharmacotherapy\u003c/h2\u003e\n\u003cp\u003eAmong current smokers, 90% received medical assistant counseling during the study period, 73% received provider counseling, 17% received behavioral assistant counseling, and 12% received pharmacotherapy.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n\u003ch2\u003eRates of Cessation Attempts\u003c/h2\u003e\n\u003cp\u003eWithin the cohort of current smokers at the index visit, 26% made a recent smoking cessation attempt at either visit 2 or 3 (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Academic, hospital-based clinics had some of the highest cessation attempt rates. Among those who had made a cessation attempt at visit 2, on average 45% relapsed at visit 3 (range 0\u0026ndash;50%) (Appendix 3).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n\u003ch2\u003eFactors Associated with Recent Smoking Cessation Attempts\u003c/h2\u003e\n\u003cp\u003eAll forms of smoking cessation counseling were significantly associated with lower odds of making a cessation attempt. Receipt of smoking cessation pharmacotherapy did not reach statistical significance. Among patient factors, only older age was associated with lower odds of making a cessation attempt (Adjusted Odds Ratio [AOR] 0.99, 95% CI 0.98\u0026ndash;0.99). Factors associated with higher odds of making a cessation attempt included female gender (AOR 1.25, 95% CI 1.10\u0026ndash;1.41), American Indian/Alaskan Native (AOR 1.82, 95% CI 1.10\u0026ndash;3.02) and Latinx/Hispanic ethnicity compared to Non-Hispanic White (AOR 1.31, 95% CI 1.08\u0026ndash;1.60), or Spanish as the primary language (AOR 1.44, 95% CI 1.14\u0026ndash;1.81). Those with depression (AOR 1.18, 95% CI 1.05\u0026ndash;1.33) and ischemic heart disease (AOR 1.36, 95% CI 1.06\u0026ndash;1.74) also had higher odds of making a cessation attempt.\u003c/p\u003e\n\u003cp\u003eAcross clinics, compared to Clinic 1 (a downtown clinic predominantly serving those experiencing homelessness), all academic, hospital-based clinics except Clinic 4 were associated with making recent cessation attempts and had some of the highest odds ratios across all 15 clinics.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n\u003ch2\u003eDemographic and Referral Characteristics among Smokers with Specific Comorbidities\u003c/h2\u003e\n\u003cp\u003eAmong current smokers with available EHR comorbidity data, we stratified patients based on the presence of comorbidities of hypertension (N\u0026thinsp;=\u0026thinsp;3,441), diabetes (N\u0026thinsp;=\u0026thinsp;1,446), depression (N\u0026thinsp;=\u0026thinsp;2,903), and HIV (N\u0026thinsp;=\u0026thinsp;1,140) (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). Current smokers in all comorbidity groups were on average middle aged (though those with HIV were slightly younger) and predominantly male.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab3\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eDemographic and Referral Characteristics among Smokers with Hypertension, Diabetes, Depression, HIV in San Francisco Health Network clinics\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003eMean (SD) or n (%)\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eSmokers with Hypertension\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(N\u0026thinsp;=\u0026thinsp;3,441)\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eSmokers with Diabetes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(N\u0026thinsp;=\u0026thinsp;1,446)\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eSmokers with Depression\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(N\u0026thinsp;=\u0026thinsp;2,903)\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eSmokers with HIV\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(N\u0026thinsp;=\u0026thinsp;1,140)\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e58.5 (10.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e57.6 (10.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e51.5 (13.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e48.0 (11.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eGender\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2,342 (68%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1,025 (71%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1,793 (62%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e975 (86%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFemale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1,099 (32%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e421 (29%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1,110 (38%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e165 (14%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eRace/ethnicity\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHispanic\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e384 (11%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e249 (17%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e472 (16%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e201 (18%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAmerican Indian or Alaska Native\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e28 (1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6 (0.4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e39 (1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e30 (3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAsian\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e551 (16%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e316 (22%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e283 (10%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e58 (5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBlack/African American\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1,501 (44%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e533 (37%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e982 (34%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e346 (31%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNative Hawaiian or other Pacific Island\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e44 (1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e33 (2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e30 (1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3 (0.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWhite\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e761 (22%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e238 (17%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e935 (33%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e428 (38%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOther\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e148 (4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e62 (4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e123 (4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e57 (5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eInsurance\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHealthy San Francisco\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e84 (3^)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e70 (6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e78 (3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e21 (2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMedi-Cal\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1,494 (50%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e626 (50%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1582 (63%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e620 (65%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMedicare\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e977 (33%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e359 (29%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e608 (24%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e235 (24%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePrivate\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e20 (1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11 (1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e26 (1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15 (2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOther coverage\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e329 (11%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e144 (12%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e161 (6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e34 (4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUninsured\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e89 (3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e30 (2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e54 (2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e30 (3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eClinic\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eClinic 1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e705 (20%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e257 (18%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e628 (22%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e206 (18%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eClinic 2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3 (0.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9 (0.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eClinic 3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e399 (12%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e170 (12%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e327 (11%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12 (1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eClinic 4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e187 (5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e46 (3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e322 (11%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e783 (69%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eClinic 5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e465 (14%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e253 (18%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e352 (12%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9 (1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eClinic 6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e163 (5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e72 (5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e166 (6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e46 (4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eClinic 7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4 (0.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1 (0.09%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eClinic 8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e174 (5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e100 (7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e77 (3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eClinic 9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e192 (6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e61 (4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e117 (4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10 (1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eClinic 10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4 (0.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7 (1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eClinic 11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e231 (7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e93 (6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e234 (8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e17 (1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eClinic 12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e155 (5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e70 (5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e116 (4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eClinic 13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e157 (5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e62 (4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e150 (5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3 (0.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eClinic 14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e189 (5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e100 (7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e139 (5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1 (0.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eClinic 15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e421 (12%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e162 (11%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e258 (9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e45 (4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eReferral characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMedical assistant counseling\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3,266 (95%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1,383 (96%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2,750 (95%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1,049 (92%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eProvider counseling\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2,753 (80%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1,154 (80%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2,297 (79%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e843 (74%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBehavioral assistant counseling\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e814 (24%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e328 (23%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e663 (23%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e82 (7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePharmacotherapy\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e595 (17%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e272 (19%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e519 (18%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e202 (18%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eSmoking Cessation Attempts\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e683 (23%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e323 (26%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e659 (26%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e208 (21%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAsian smokers were more represented among populations with hypertension (16%) and diabetes (22%) and less so among those with depression (10%) or HIV (5%). Latinx smokers were more represented among groups with diabetes (17%), depression (17%), and HIV (18%). Black/African American smokers were well represented in all comorbidity groups, especially among those with hypertension (44%). White smokers were well represented in all groups, especially among those with depression (33%) and HIV (38%).\u003c/p\u003e\n\u003cp\u003eThere were high rates of current smokers across comorbidity groups receiving medical assistant (range 92\u0026ndash;96%) and provider counseling (74\u0026ndash;80%). There was much lower delivery of behavioral assistant counseling (range 7\u0026ndash;24%), especially among current smokers with HIV. Notably, those with HIV had consistently the lowest rates of receiving any type of cessation counseling. All comorbidity groups had similar low percentages of receiving cessation pharmacotherapy (17\u0026ndash;19%). Cessation rates across comorbidity groups were also similar (21\u0026ndash;26%).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eAmong 51,554 individuals across 15 safety-net primary care clinics, we found 23% were current smokers, and 26% made a recent cessation attempt. Safety-net clinics delivered medical assistant and provider counseling at high rates, though rates were much lower for behavioral assistant counseling and pharmacotherapy. Contrary to our hypothesis, individuals receiving any type of cessation counseling were less likely to make a cessation attempt. This was dissimilar to our prior work, which found higher odds of making a cessation attempt in those who received medical assistant counseling and provider counseling, albeit the prior study was limited to only four clinic sites with a much smaller study sample.\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eOur results highlight how safety-net clinics are able to adequately deliver basic cessation interventions such as provider and medical assistant counseling, and that enhancements in EHR functions allowed demonstration of cessation service delivery. However, delivery of cessation services may not always correlate with cessation attempts, especially in the context of large health systems with diverse subpopulations. For subpopulations with high burden of comorbidities, basic cessation services may be insufficient, highlighting a need for more intensive efforts.\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e Such efforts may include counseling from care providers of multiple disciplines, combining referrals from different encounters or providers, and streamlined infrastructure to ensure efficient delivery of cessation resources.\u003c/p\u003e\n\u003cp\u003eSeveral opportunities for interventions among subgroups exist. Although members of Black/African American, Latinx, and Asian communities are less likely to ever smoke and are more likely to be lighter smokers than their White counterparts, they also face disproportionately worse smoking-related health outcomes.\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e And despite higher interest in quitting than White individuals and past-year quit attempts, Black/African American individuals have lower rates of sustained cessation. These racial disparities can be attributed to structures of systemic racism, including barriers to accessing care, lower receipt of cessation counseling and pharmacotherapy, and increased targeted marketing of tobacco products to racial/ethnic minorities, making sustained cessation more challenging among these communities.\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eWe found communities of color were well represented across comorbidity groups, with Asian smokers well represented among groups with hypertension and diabetes, Latinx smokers represented among those with diabetes, depression, and HIV, and Black/African American represented across all comorbidity groups, especially hypertension. As efforts in addressing diabetes, hypertension, depression, and HIV have all displayed success in improving health outcomes by using a chronic disease management framework, pairing smoking cessation with other chronic disease management efforts may help address racial/ethnic disparities in smoking outcomes.\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e Such interventions include telephone or in-person outreach to targeted populations, linking cessation counseling with efforts to improve blood pressure or diabetes care targets, or community engagement practices to inform cessation interventions. Latinx and Non-English speaking patients also had higher odds of recent cessation attempts, highlighting the importance of culturally informed and language concordant cessation counseling and resources.\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e More intensive efforts would align with equity goals to reduce racial disparities across health systems.\u003c/p\u003e\n\u003cp\u003eOur findings demonstrate how EHRs can be an effective tool for identifying smokers and delivering basic smoking cessation services within the context of rapid cycle quality improvement work. In the past decade, financial incentive programs for meaningful use of EHRs have increased tobacco screening, documentation of smoking status, and delivery of cessation services in safety-net settings.\u003csup\u003e11,27\u0026minus;29\u003c/sup\u003e Additionally, the EHR has shown to be effective in rapidly identifying factors associated with cessation attempts and the receipt of referral services, which could be used to drive quality improvement activities to improve health outcomes.\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eIn the face of many competing priorities, health systems are required to meet minimum criteria to obtain reimbursement and incentives from public insurers. For example, The Public Hospital Redesign and Incentives in Medi-Cal program (PRIME) requires evidence-based quality improvement goals for clinics, including screening for smoking status and counseling every two years.\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e However best practices guidelines recommend assessments at every clinical encounter to optimize chances of cessation,\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e highlighting how more intensive interventions than those required by public insurers may be needed to improve patient outcomes. Therefore, clinics should acknowledge the need to meet minimum requirements for reimbursement, but also to take measures to strive for best practice recommendations. Health systems can do so by streamlining efforts, including assigning responsibilities to each health team member for providing smoking cessation services or providing guidance on how frequently these services should be provided to avoid redundancy and waste of resources. The EHR is also advantageous in identifying populations that need these intensive interventions, and the PRECEDE-PROCEED model can be used to develop interventions in these contexts. For example, the SFHN implemented an EPIC Enterprise EHR in August 2020. Our evaluation using the PRECEDE-PROCEED is therefore timely in providing critical information to developing system-level approaches to support cessation efforts throughout the network. Already, efforts from this work have led to creation of a tobacco registry that includes a better screening tool for tobacco use embedded within the new EHR and templates to document counseling interventions. The registry can be used to track receipt of cessation services and drive practice changes in delivery of cessation care.\u003c/p\u003e\n\u003cp\u003eIn our study, we found that only a quarter of current smokers made a recent cessation attempt, and of those who made a smoking cessation attempt at visit 2, about half of them relapsed by visit 3. These rates of cessation attempts were lower than the estimated 44% of smoking cessation attempts in the past year made by the general US population.\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e Little is known about the rates of cessation attempts in primary care settings, with few studies estimating roughly 36\u0026ndash;39% of patients making a recent cessation attempt and 15\u0026ndash;20% of patients maintain cessation at one year.\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e Ours was one of the few studies evaluating cessation attempts and relapse rates at clinic or system levels within a safety-net system. Because most smokers who attempt cessation are likely to relapse with high average lifetime number of quit attempts before sustained cessation,\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e program initiatives should pay increasing attention toward sustaining cessation attempts and streamlining interventions to determine which groups require more intensive efforts.\u003c/p\u003e\n\u003cp\u003eThere are several limitations to our study. EHR data relied on patient self-report, and smoking status was not biochemically verified, leading to a potential misclassification bias. However, our repeated assessments of smoking status over time may have reduced potential misclassifications. By excluding people with missing smoking status in the analysis, we may have also introduced some bias.\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e Still, our large sample size may have protected against this and allows our data to be generalizable, as the inclusion of a diverse array of patients and clinics may be representative of other safety-net settings. The quality of smoking status data collection could have varied across clinic sites, though all clinics had the same EHR with a structured format for data collection.\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e Finally, for some clinics especially those serving young adult populations, the actual numbers of patients who attempted recent cessation were small, leading to inflated percentages of relapse in these clinics.\u003c/p\u003e\n\u003cp\u003eIn conclusion, the EHR can be used to efficiently understand and identify opportunities for improvement in delivering smoking cessation services, especially in subpopulations that may require more intensive, directed efforts to achieve sustained cessation.\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e Safety-net providers and clinic leaders could consider using the EHR to enhance the reach and efficacy of smoking cessation services, and target subpopulations with high needs in order to reduce racial and health disparities in safety-net settings.\u003c/p\u003e"},{"header":"Declarations","content":"\u003col\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate: \u003c/strong\u003eThis study was approved by the University of California, San Francisco Committee on Human Research (#18-26398).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication: \u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests: \u003c/strong\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eFunding: \u003c/strong\u003eThis study was supported by the National Heart, Lung and Blood Institute (R38 HL143581) and the Tobacco-Related Disease Research Program (28CP-0038). The funding agencies had no role in study design, data collection, analysis, the decision to publish, or the preparation of the manuscript.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eData availability:\u003c/strong\u003e The datasets generated during and/or analyzed during the current study are not publicly available due to ongoing data collection and analysis but are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors contributions: \u003c/strong\u003eV., H.R., and E.C. conceptualized the design of the study. T.L. performed data analysis with guidance from M.V. and L.S. L.S. and M.V. wrote the manuscript with support from K.C., E.S., T.L., E.C., and H.R. All authors contributed to the final version of the manuscript.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements: \u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003ePrior presentations\u003c/strong\u003e: This work was presented as a poster presentation at the San Francisco Bay Area Collaborative Research Network 2020 Annual Meeting.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eUnited States Surgeon General. The Health Consequences of Smoking -- 50 Years of progress: A Report of the Surgeon General: (510072014-001). Published online 2014. doi:10.1037/e510072014-001\u003c/li\u003e\n\u003cli\u003eJamal A, Phillips E, Gentzke AS. Current Cigarette Smoking Among Adults \u0026mdash; United States, 2016. \u003cem\u003eMMWR Morb Mortal Wkly Rep\u003c/em\u003e. 2018;67:53-59. doi:10.15585/mmwr.mm6702a1\u003c/li\u003e\n\u003cli\u003eWeinberger AH, Gbedemah M, Wall MM, Hasin DS, Zvolensky MJ, Goodwin RD. Cigarette use is increasing among people with illicit substance use disorders in the United States, 2002-14: emerging disparities in vulnerable populations. \u003cem\u003eAddiction\u003c/em\u003e. 2018;113(4):719-728. doi:10.1111/add.14082\u003c/li\u003e\n\u003cli\u003eStead LF, Perera R, Bullen C, et al. Nicotine replacement therapy for smoking cessation. \u003cem\u003eCochrane Database Syst Rev\u003c/em\u003e. 2012;11:CD000146. doi:10.1002/14651858.CD000146.pub4\u003c/li\u003e\n\u003cli\u003eStead LF, Buitrago D, Preciado N, Sanchez G, Hartmann-Boyce J, Lancaster T. Physician advice for smoking cessation. \u003cem\u003eCochrane Database Syst Rev\u003c/em\u003e. 2013;(5):CD000165. doi:10.1002/14651858.CD000165.pub4\u003c/li\u003e\n\u003cli\u003ePark ER, Gareen IF, Japuntich S, et al. Primary Care Provider-Delivered Smoking Cessation Interventions and Smoking Cessation Among Participants in the National Lung Screening Trial. \u003cem\u003eJAMA Intern Med\u003c/em\u003e. 2015;175(9):1509-1516. doi:10.1001/jamainternmed.2015.2391\u003c/li\u003e\n\u003cli\u003eGubner NR, Williams DD, Chen E, et al. Recent cessation attempts and receipt of cessation services among a diverse primary care population - A mixed methods study. \u003cem\u003ePrev Med Rep\u003c/em\u003e. 2019;15:100907. doi:10.1016/j.pmedr.2019.100907\u003c/li\u003e\n\u003cli\u003eBernstein SL, Weiss J, DeWitt M, et al. A randomized trial of decision support for tobacco dependence treatment in an inpatient electronic medical record: clinical results. \u003cem\u003eImplement Sci\u003c/em\u003e. 2019;14(1):8. doi:10.1186/s13012-019-0856-8\u003c/li\u003e\n\u003cli\u003eBlumenthal D, Tavenner M. The \u0026ldquo;meaningful use\u0026rdquo; regulation for electronic health records. \u003cem\u003eN Engl J Med\u003c/em\u003e. 2010;363(6):501-504. doi:10.1056/NEJMp1006114\u003c/li\u003e\n\u003cli\u003eCenters for Medicare \u0026amp; Medicaid Services (CMS), HHS. Medicare and Medicaid programs; electronic health record incentive program. Final rule. \u003cem\u003eFed Regist\u003c/em\u003e. 2010;75(144):44313-44588.\u003c/li\u003e\n\u003cli\u003eBailey SR, Heintzman JD, Marino M, et al. Smoking-Cessation Assistance: Before and After Stage 1 Meaningful Use Implementation. \u003cem\u003eAm J Prev Med\u003c/em\u003e. 2017;53(2):192-200. doi:10.1016/j.amepre.2017.02.006\u003c/li\u003e\n\u003cli\u003eHerbst N, Wiener RS, Helm ED, et al. Effectiveness of an Opt-Out Electronic-Heath Record-based Tobacco Treatment Consult Service at an Urban Safety-Net Hospital. \u003cem\u003eChest\u003c/em\u003e. Published online May 16, 2020. doi:10.1016/j.chest.2020.04.062\u003c/li\u003e\n\u003cli\u003eFiore M, Adsit R, Zehner M, et al. An electronic health record\u0026ndash;based interoperable eReferral system to enhance smoking Quitline treatment in primary care. \u003cem\u003eJ Am Med Inform Assoc\u003c/em\u003e. 2019;26(8-9):778-786. doi:10.1093/jamia/ocz044\u003c/li\u003e\n\u003cli\u003eZhu S-H, Anderson CM, Tedeschi GJ, et al. Evidence of Real-World Effectiveness of a Telephone Quitline for Smokers. \u003cem\u003eNew England Journal of Medicine\u003c/em\u003e. 2002;347(14):1087-1093. doi:10.1056/NEJMsa020660\u003c/li\u003e\n\u003cli\u003eGreen LW, Kreuter MW. \u003cem\u003eHealth Program Planning: An Educational and Ecological Approach\u003c/em\u003e. 4th edition. McGraw-Hill; 2005. Accessed June 24, 2020. https://www.researchgate.net/publication/301749054_Green_LW_Kreuter_MW_Health_Program_Planning_An_Educational_and_Ecological_Approach_4th_Edition_New_York_McGraw-Hill_2005\u003c/li\u003e\n\u003cli\u003eJose T, Ohde JW, Hays JT, Burke MV, Warner DO. Design and Pilot Implementation of an Electronic Health Record-Based System to Automatically Refer Cancer Patients to Tobacco Use Treatment. \u003cem\u003eInt J Environ Res Public Health\u003c/em\u003e. 2020;17(11). doi:10.3390/ijerph17114054\u003c/li\u003e\n\u003cli\u003eTrinidad DR, P\u0026eacute;rez-Stable EJ, White MM, Emery SL, Messer K. A Nationwide Analysis of US Racial/Ethnic Disparities in Smoking Behaviors, Smoking Cessation, and Cessation-Related Factors. \u003cem\u003eAm J Public Health\u003c/em\u003e. 2011;101(4):699-706. doi:10.2105/AJPH.2010.191668\u003c/li\u003e\n\u003cli\u003eTrinidad DR, P\u0026eacute;rez-Stable EJ, Emery SL, White MM, Grana RA, Messer KS. Intermittent and light daily smoking across racial/ethnic groups in the United States. \u003cem\u003eNicotine \u0026amp; Tobacco Research\u003c/em\u003e. 2009;11(2):203-210. doi:10.1093/ntr/ntn018\u003c/li\u003e\n\u003cli\u003eSakuma K-LK, Felicitas-Perkins JQ, Blanco L, et al. Tobacco use disparities by racial/ethnic groups: California compared to the United States. \u003cem\u003ePreventive Medicine\u003c/em\u003e. 2016;91:224-232. doi:10.1016/j.ypmed.2016.08.035\u003c/li\u003e\n\u003cli\u003eBabb S. Quitting Smoking Among Adults \u0026mdash; United States, 2000\u0026ndash;2015. \u003cem\u003eMMWR Morb Mortal Wkly Rep\u003c/em\u003e. 2017;65. doi:10.15585/mmwr.mm6552a1\u003c/li\u003e\n\u003cli\u003eCDCTobaccoFree. African Americans and Tobacco Use. Centers for Disease Control and Prevention. Published November 18, 2019. Accessed August 12, 2020. https://www.cdc.gov/tobacco/disparities/african-americans/index.htm\u003c/li\u003e\n\u003cli\u003eCDC. Hispanics/Latinos and Tobacco Use. Centers for Disease Control and Prevention. Published December 17, 2018. Accessed August 12, 2020. https://www.cdc.gov/tobacco/disparities/hispanics-latinos/index.htm\u003c/li\u003e\n\u003cli\u003eBailey SR, Heintzman J, Jacob RL, Puro J, Marino M. Disparities in Smoking Cessation Assistance in US Primary Care Clinics. \u003cem\u003eAm J Public Health\u003c/em\u003e. 2018;108(8):1082-1090. doi:10.2105/AJPH.2018.304492\u003c/li\u003e\n\u003cli\u003eBailey SR, Stevens VJ, Fortmann SP, et al. Long-Term Outcomes From Repeated Smoking Cessation Assistance in Routine Primary Care: \u003cem\u003eAmerican Journal of Health Promotion\u003c/em\u003e. Published online March 13, 2018. doi:10.1177/0890117118761886\u003c/li\u003e\n\u003cli\u003eStevens VJ, Solberg LI, Bailey SR, et al. Assessing Trends in Tobacco Cessation in Diverse Patient Populations. \u003cem\u003eNicotine Tob Res\u003c/em\u003e. 2016;18(3):275-280. doi:10.1093/ntr/ntv092\u003c/li\u003e\n\u003cli\u003eParker MM, Fern\u0026aacute;ndez A, Moffet HH, Grant RW, Torreblanca A, Karter AJ. Association of Patient-Physician Language Concordance and Glycemic Control for Limited-English Proficiency Latinos With Type 2 Diabetes. \u003cem\u003eJAMA Intern Med\u003c/em\u003e. 2017;177(3):380-387. doi:10.1001/jamainternmed.2016.8648\u003c/li\u003e\n\u003cli\u003eKruse G, Chang Y, Kelley JH, Einbinder J, Rigotti NA. Healthcare System Effects of Pay-for-performance for Smoking Status Documentation. Published online 2014:15.\u003c/li\u003e\n\u003cli\u003ePolubriaginof F, Salmasian H, Albert DA, Vawdrey DK. Challenges with Collecting Smoking Status in Electronic Health Records. \u003cem\u003eAMIA Annu Symp Proc\u003c/em\u003e. 2018;2017:1392-1400. Accessed June 23, 2020. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5977725/\u003c/li\u003e\n\u003cli\u003eVidrine JI, Shete S, Li Y, et al. The Ask\u0026ndash;Advise\u0026ndash;Connect Approach for Smokers in a Safety Net Healthcare System: A Group-Randomized Trial. \u003cem\u003eAmerican Journal of Preventive Medicine\u003c/em\u003e. 2013;45(6):737-741. doi:10.1016/j.amepre.2013.07.011\u003c/li\u003e\n\u003cli\u003eKaslow AA, Romano PS, Schwarz E, Shaikh U, Tong EK. Building and Scaling-up California Quits: Supporting Health Systems Change for Tobacco Treatment. \u003cem\u003eAmerican Journal of Preventive Medicine\u003c/em\u003e. 2018;55(6):S214-S221. doi:10.1016/j.amepre.2018.07.045\u003c/li\u003e\n\u003cli\u003eFiore MC, Jaen CR, Baker TB, et al. \u003cem\u003eTreating Tobacco Use and Dependence: 2008 Update\u003c/em\u003e. US Department of Health and Human Services. Public Health Service.; 2008. Accessed August 12, 2020. https://www.ncbi.nlm.nih.gov/books/NBK63952/\u003c/li\u003e\n\u003cli\u003eKahende JW, Malarcher AM, Teplinskaya A, Asman KJ. Quit Attempt Correlates among Smokers by Race/Ethnicity. \u003cem\u003eInt J Environ Res Public Health\u003c/em\u003e. 2011;8(10):3871-3888. doi:10.3390/ijerph8103871\u003c/li\u003e\n\u003cli\u003eWadland WC, St\u0026ouml;ffelmayr B, Berger E, Crombach A, Ives K. Enhancing smoking cessation rates in primary care. \u003cem\u003eJ Fam Pract\u003c/em\u003e. 1999;48(9):711-718.\u003c/li\u003e\n\u003cli\u003eBold KW, Rasheed AS, McCarthy DE, Jackson TC, Fiore MC, Baker TB. Rates and Predictors of Renewed Quitting After Relapse During a One-Year Follow-Up Among Primary Care Patients. \u003cem\u003eAnn Behav Med\u003c/em\u003e. 2015;49(1):128-140. doi:10.1007/s12160-014-9627-6\u003c/li\u003e\n\u003cli\u003eGarc\u0026iacute;a-Rodr\u0026iacute;guez O, Secades-Villa R, Fl\u0026oacute;rez-Salamanca L, Okuda M, Liu S-M, Blanco C. Probability and predictors of relapse to smoking: Results of the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC). \u003cem\u003eDrug Alcohol Depend\u003c/em\u003e. 2013;132(3):479-485. doi:10.1016/j.drugalcdep.2013.03.008\u003c/li\u003e\n\u003cli\u003eChaiton M, Diemert L, Cohen JE, et al. Estimating the number of quit attempts it takes to quit smoking successfully in a longitudinal cohort of smokers. \u003cem\u003eBMJ Open\u003c/em\u003e. 2016;6(6). doi:10.1136/bmjopen-2016-011045\u003c/li\u003e\n\u003cli\u003eAuerbach A, Bates DW. Introduction: Improvement and Measurement in the Era of Electronic Health Records. \u003cem\u003eAnnals of Internal Medicine\u003c/em\u003e. 2020;172(11_Supplement):S69-S72. doi:10.7326/M19-0870\u003c/li\u003e\n\u003cli\u003eKawamoto K, McDonald CJ. Designing, Conducting, and Reporting Clinical Decision Support Studies: Recommendations and Call to Action. \u003cem\u003eAnn Intern Med\u003c/em\u003e. 2020;172(11_Supplement):S101-S109. doi:10.7326/M19-0875\u003c/li\u003e\n\u003cli\u003eZheng K, Ratwani RM, Adler-Milstein J. Studying Workflow and Workarounds in Electronic Health Record-Supported Work to Improve Health System Performance. \u003cem\u003eAnn Intern Med\u003c/em\u003e. 2020;172(11_Supplement):S116-S122. doi:10.7326/M19-0871\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Appendix ","content":"\u003cp\u003e\u0026nbsp;Appendix Table 1. Demographics by type of clinics\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Taba\" border=\"1\"\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eAcademic clinics\u003c/div\u003e\n\u003cdiv class=\"SimplePara\"\u003e(N\u0026thinsp;=\u0026thinsp;21314)\u003c/div\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eCommunity clinics\u003c/div\u003e\n\u003cdiv class=\"SimplePara\"\u003e(N\u0026thinsp;=\u0026thinsp;30240)\u003c/div\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Bold\"\u003eAge\u003c/span\u003e\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e50.2 (16.5)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e52.8 (15.5)\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Bold\"\u003eGender\u003c/span\u003e\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eMale\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e10347 (49%)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e14300 (47%)\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eFemale\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e10967 (51%)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e15940 (53%)\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Bold\"\u003eRace/ethnicity\u003c/span\u003e\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eHispanic\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e8701 (41%)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e6388 (21%)\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eAmerican Indian or Alaska Native\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e164 (1%)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e144 (0.5%)\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eAsian\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e4337 (21%)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e10686 (36%)\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eBlack/African American\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e2558 (12%)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e5133 (17%)\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eNative Hawaiian or other Pacific Island\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e171 (1%)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e338 (1%)\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eWhite\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e3358 (16%)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e6029 (20%)\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eOther\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e1740 (8%)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e1057 (4%)\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Bold\"\u003eLanguage\u003c/span\u003e\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eEnglish\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e12359 (58%)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e17406 (58%)\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eSpanish\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e6200 (29%)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e4360 (14%)\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eCantonese/Mandarin/Chinese\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e1374 (7%)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e6804 (23%)\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eVietnamese\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e448 (2%)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e271 (1%)\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eRussian\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e192 (1%)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e519 (2%)\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eTagalog\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e205 (1%)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e192 (1%)\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eKorean\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e18 (0.1%)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e221 (1%)\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eArabic\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e94 (0.4%)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e51 (0.2%)\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eOther\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e263 (1%)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e300 (1%)\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Bold\"\u003eInsurance\u003c/span\u003e\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eHealthy San Francisco\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e1910 (10%)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e1809 (7%)\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eMedi-Cal\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e10262 (54%)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e12677 (47%)\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eMedicare\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e3784 (20%)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e5417 (20%)\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003ePrivate\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e304 (2%)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e295 (1%)\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eOther coverage\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e2092 (11%)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e6094 (23%)\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eUninsured\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e551 (3%)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e624 (2%)\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Bold\"\u003eRecent Smoking Cessation Attempt*\u003c/span\u003e\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e891 (33%)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e1017 (22%)\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"3\"\u003e*For recent cessation attempts, only individuals with at least three primary care encounters with smoking status were included in this measure, leading to N\u0026thinsp;=\u0026thinsp;2664 for academic clinics and N\u0026thinsp;=\u0026thinsp;4724 for community clinics.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAppendix Table 2. International Classification of Diseases 9 or 10 Diagnoses Extracted to Characterize Presence of Comorbidities\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tabb\" border=\"1\"\u003e\n\u003cthead\u003e\n\u003ctr style=\"height: 13px;\"\u003e\n\u003cth style=\"height: 13px;\" align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eDiagnosis\u003c/div\u003e\n\u003c/th\u003e\n\u003cth style=\"height: 13px;\" align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eICD9 code\u003c/div\u003e\n\u003c/th\u003e\n\u003cth style=\"height: 13px;\" align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eICD10 code\u003c/div\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr style=\"height: 13px;\"\u003e\n\u003ctd style=\"height: 13px;\" align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eAsthma\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 13px;\" align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e493\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 13px;\" align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eJ45\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 26px;\"\u003e\n\u003ctd style=\"height: 26px;\" align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eChronic Obstructive Pulmonary Disease\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 26px;\" align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e491\u0026ndash;494, 496\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 26px;\" align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eJ41-44, J47\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 13px;\"\u003e\n\u003ctd style=\"height: 13px;\" align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eDepression\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 13px;\" align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e290, 296, 298, 300, 301, 309, 311\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 13px;\" align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eF01, F32-F34, F43\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 13px;\"\u003e\n\u003ctd style=\"height: 13px;\" align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eDiabetes\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 13px;\" align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e250, 357, 362, 366, 648\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 13px;\" align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eE10, E11, E13, Q24\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 13px;\"\u003e\n\u003ctd style=\"height: 13px;\" align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eHIV\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 13px;\" align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e042, V08\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 13px;\" align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eB20, Z21\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 13px;\"\u003e\n\u003ctd style=\"height: 13px;\" align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eHyperlipidemia\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 13px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 13px;\" align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eE78\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 13px;\"\u003e\n\u003ctd style=\"height: 13px;\" align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eHypertension\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 13px;\" align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e401\u0026ndash;404\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 13px;\" align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eI10-13\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 26px;\"\u003e\n\u003ctd style=\"height: 26px;\" align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eIschemic vascular disease\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 26px;\" align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e411, 413, 414, 429, 433, 434, 437, 440, 444, 445\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 26px;\" align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eI20, I24, I25, I63, I65-67, I70, I75, T82\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 13px;\"\u003e\n\u003ctd style=\"height: 13px;\" align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eHeart failure\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 13px;\" align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e398, 402, 404, 428\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 13px;\" align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eI09, I11, I13, I50\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 26px;\"\u003e\n\u003ctd style=\"height: 26px;\" align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eKidney disease\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 26px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 26px;\" align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eA18, A52, B52, C64, C68, D30, D41, D59, E08-E11, E13, E74, I12, I13, I70, I72, K76, M10, M32, M35, N00-08, N13-19, N25, N26, Q61, Q62, R94\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAppendix Table 3. The relapse rate at visit 3 among smokers who made recent quit attempts in visit 2\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tabc\" border=\"1\"\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003en (%)\u003c/div\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eOverall\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e536 (45%)\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eBy clinic\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eClinic 1\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e62 (50%)\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eClinic 2\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e3 (50%)\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eClinic 3\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e105 (45%)\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eClinic 4\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e37 (45%)\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eClinic 5\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e112 (50%)\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eClinic 6\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e31 (33%)\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eClinic 7\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e1 (50%)\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eClinic 8\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e22 (50%)\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eClinic 9\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e10 (37%)\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eClinic 10\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0 (0%)\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eClinic 11\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e32 (45%)\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eClinic 12\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e29 (47%)\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eClinic 13\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e21 (48%)\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eClinic 14\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e30 (45%)\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eClinic 15\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e41 (39%)\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"smoking cessation, electronic medical records, primary care, safety-net clinics, quality improvement","lastPublishedDoi":"10.21203/rs.3.rs-115150/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-115150/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eSmoking cessation rates are low in safety-net settings, contributing to high smoking-related morbidity and mortality. Understanding factors associated with cessation attempts can inform interventions. \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eObjective: \u003c/strong\u003eTo evaluate factors associated with smoking cessation attempts. \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eDesign: \u003c/strong\u003eRetrospective analysis using electronic health record (EHR) data on individuals with at least three primary care encounters from 2016 to 2019 in the San Francisco Health Network (SFHN), a network of clinics serving publicly insured and uninsured residents in San Francisco.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eParticipants: \u003c/strong\u003ePatients engaged in primary care in the San Francisco Health Network.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eMain Measures: \u003c/strong\u003eThe outcome was recent cessation attempt, defined as change in smoking status from “current smoker” at the index visit to “former smoker” at visit 2 or 3. We measured demographics, tobacco-related comorbidities, and cessation treatment characteristics (i.e., counseling and pharmacotherapy). To better characterize subpopulations that may benefit from targeted interventions, we described characteristics of smokers with hypertension, depression, diabetes, or HIV.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eKey Results: \u003c/strong\u003eOf the 51,554 adults identified across 15 SFHN primary care clinics, 11,622 (22.7%) were current smokers. Approximately 26% of smokers made a recent cessation attempt. Medical assistant (90%) and provider counseling (73%) rates were high, while behavioral assistant counseling rate (17%) was low. All counseling types had lower odds of cessation attempts in multivariable analysis. Smokers with depression (AOR 1.18, 95%CI 1.05-1.33) and ischemic heart disease (AOR 1.36, 95%CI 1.06-1.74) had higher odds of attempts. Among comorbidity groups, cessation attempts ranged from 21-26%, and smokers with HIV received the lowest rates of cessation counseling. \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eAlthough rates of basic cessation counseling were high, efforts were associated with lower odds of making a cessation attempt. Using intensive interventions to target populations with comorbidities could be opportunities to increase cessation engagement.\u0026nbsp;\u0026nbsp;\u003c/p\u003e","manuscriptTitle":"Delivery of Smoking Cessation Services and Cessation Attempts Across a Public, Safety-Net Primary Care System","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2020-12-01 17:29:32","doi":"10.21203/rs.3.rs-115150/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revision","date":"2021-03-19T00:00:00+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2021-03-12T00:00:00+00:00","index":3,"fulltext":"Recommendation: Major revisions required\nForm responses:\n---\n\nComments to Author:\n---\nPEER REVIEWER ASSESSMENTS:\n\nOBJECTIVE - Full research articles: is there a clear objective that addresses a testable research question(s) (brief or other article types: is there a clear objective)?\nYes - there is a clear objective\n\nDESIGN - Is the current approach (including controls and analysis protocols) appropriate for the objective?\nNo - there are major issues\n\nEXECUTION - Are the experiments and analyses performed with technical rigor to allow confidence in the results?\nNo - there are major issues\n\nSTATISTICS - Is the use of statistics in the manuscript appropriate?\nNo - there are issues with the statistics in the study\n\nINTERPRETATION - Is the current interpretation/discussion of the results reasonable and not overstated?\nNo - there are major issues\n\nOVERALL MANUSCRIPT POTENTIAL - Is the current version of this work technically sound? If not, can revisions be made to make the work technically sound?\nMaybe - with major revisions\n\nPEER REVIEWER COMMENTS:\n\nGENERAL COMMENTS:\nThe authors have undertaken an interesting attempt to analyze factors associated with quitting smoking among patients of a network of clinics. However, the results contradict what the authors expected to see and what is plausible. I assume that this happened due to several threats to validity. My impression is that the analytic design requires several rounds of refinement. Below, I try to explain how I would approach this data differently.\n\nREQUESTED REVISIONS:\nThe first important comment is related to the theoretical framework. The authors consider quit attempt their outcome, and primary exposures are cessation interventions. However, these interventions are not fully independent. Not all the smokers get these. Clinics probably have protocols regarding whom they address with these interventions. Most likely, these are smokers with higher dependence, difficulty quitting, and more cigarettes per day. Consequently, smokers with these characteristics more likely get cessation interventions and have lower probability of quitting. If the data on nicotine dependence or number of cigarettes per day is available, it is worth controlling for.\n\nThe second important observation is related to Hispanic ethnicity and Spanish as a primary language. In Table 1, the authors present characteristics of smokers and non-smokers. Interestingly, patients with Hispanic ethnicity are shown to be less likely smokers. In Table 3, they are shown to have more likely attempted quitting smoking. These two findings are also likely to be associated with lower level of nicotine dependence in this population group than the others. In the Discussion, the authors comment on other ethnic groups, which needs to be taken into account as well, for instance in the form of interaction or stratified analysis.\n\nI have several suggestions on how to re-organize the analytic design and how to present it in tables.\n\nIn Table 1, it seems worth showing never-smokers and ex-smokers separately. This will reveal the characteristics of those who managed to quit smoking earlier.\n\nIn Table 2, it is worth showing the number of participants and percentage of those who made a quit attempt by categories of every covariate.\n\nWhat is also strange regarding Table 2, is the variable Visit. If this shows by which visit the smoking status has changed, this belongs to the dependent variable, not to the independent one and thus, should not appear among the covariates. If the authors are interested to explore characteristics of those who made quit attempt by second or third visit, an additional bivariate table can be presented for this with columns: 'keeps smoking', 'quit by the second visit', and 'quit by the third visit'.\n\nTable 2 also shows different results by clinic. As Counseling and pharmacotherapy was provided by clinics, I would also try considering interaction between the clinic and these interventions.\n\nAdditionally, it is worth taking into account at which point in time cessation interventions were provided. Was this done during the index visit or at some other point? It is also interesting to understand how the authors define each type of counseling and how they differ.\n\nThe results presented regarding the secondary objective (Among comorbidity groups, … smokers with HIV received the lowest rates of cessation counseling.) it might be worth conducting an extended regression analysis and controlling for other covariates including age, clinic, gender, level of nicotine dependence etc.\nADDITIONAL REQUESTS/SUGGESTIONS:\nINTRODUCTION: The authors mention \"warm hand offs\" approach; however, a brief explanation might be worthwhile in this regard.\n\nRESULTS: \"The sample included 51,554 individuals from 15 clinics, of whom 11,622 (23%) were current smokers...\" - this is probably related to the index encounter. If this is the case, suggest specifying this.* Publons Reviewer Recognition. Springer Nature can send verification of this review directly to Publons (a subsidiary of Clarivate Analytics). If you would like to take advantage of this service, please click on the “Yes” option below. Your name, email address, title of the reviewed manuscript, name of the journal, and date of your review submission (the “Review Data”) will then be transmitted to Publons after the final decision on the manuscript has been made. If you have already registered at Publons, they will notify you of the receipt of this review and update your profile as per your settings and their policy. If you are not registered with Publons, you will receive an email from them asking you to register in order for them to be able to recognize your review on your new profile page. Publons may use the Review Data to generate derivative metadata for the benefit of Publons and you as a reviewer, carefully considering the sensitivity of such information. For example, Publons may verify your record as a reviewer by updating your profile published on its webservice if you have registered for such service or help editors to identify candidate reviewers. Please find the details of processing in Publons’ privacy policy https://publons.com/about/terms: **No**\n* Declaration of competing interests: **This reviewer has been recruited by a partner organization, Research Square. Reviewers with declared or apparent competing interests are not utilized for these reviews. This reviewer has agreed to publication of their comments online under a Creative Commons Attribution License attributed to Research Square and was paid a small honorarium for completing the review within a specified timeframe. Honoraria for reviews such as this are paid regardless of the reviewer recommendation.**\n* Reviewer Publication Consent. I agree for my report to be made available under an Open Access Creative Commons CC-BY License (http://creativecommons.org/licenses/by/4.0) if this manuscript is accepted for publication. Any comments that I do not wish to be included in the published report have been included as confidential comments to the editor, which will not be published.: **I agree to the terms of the CC-BY 4.0 license; please publish my name with my report.**\n* Is the study design appropriate to answer the research question (including the use of appropriate controls), and are the conclusions supported by the evidence presented?: **No**\n* Are the methods sufficiently described to allow the study to be repeated?: **No**\n* Is the use of statistics and treatment of uncertainties appropriate?: **No**\n* Is the presentation of the work clear?: **No**\n* Are the images in this manuscript (including electrophoretic gels and blots) free from apparent manipulation?: **Yes**\n"},{"type":"reviewerAgreed","content":"","date":"2021-03-04T00:00:00+00:00","index":3,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2021-03-01T00:00:00+00:00","index":1,"fulltext":"Recommendation: Major revisions required\nForm responses:\n---\n\nComments to Author:\n---\nThank you for the opportunity to review your work. I found it interesting and generally well-written. Please see specific comments below.\n\nMinor comments:\n\nMy assumption is that the authors were striving for a brief Introduction section. Unfortunately, several terms are not adequately explained:\n* 1st paragraph, 3rd sentence: What is a safety-net health system? For those not in the US, this is not a known term with an inherent meaning.\n* 2nd paragraph, 3rd sentence: what is meant by, \"ownership of counselling\"? It is no doubt clear to the authors who are intimately familiar with their own work being cited, but this phrase is not clear to me and it is not explained.\n* 2nd paragraph, last sentence: what is a 'safety-net population'? same comment as above.\n\nMethods:\n* Typos?:\no Statement that four clinics are academic primary care practices but parentheses list five clinics\n\nResults section: Rates of cessation attempts. The estimates discussed are not rates. They are proportions. And as described below, use of the term\n'attempts' is misleading.\n\nResults section: Factors associated with recent smoking cessation attempts. Is it possible there was reverse coding of the outcome variable or some exposures? The inverse associations with all counselling and pharmacotherapy are indeed surprising as noted in the discussion section.\n\nDiscussion section:\n* I am not sure how your demonstrates that EHRs can be an effective tool for identifying smokers and delivering basic smoking cessation services, as stated in the discussion section. Your study identified smokers retrospectively. So, yes, the EHR was good source of data for retrospective research. But as for the EHR being an effective tool for delivering smoking cessation services…that may be true but it is not demonstrated in your study.\n* The introduction refers to the PRECEDE-PROCEED model and the methods section states that, \"we explore potential Policy, Regulatory, and Organizational (PROCEED) factors in order to facilitate the implementation of system-level interventions to increase delivery of cessation services and evaluation of such interventions.\" But this isn't picked up again and discussed further. How do the analyses presented explore policy, regulation or organizational factors? \n\nMajor comments:\n\nI find the objective, the description of the analytic population and associated analyses, confused.\n* The objective, that appears in the abstract only, is to evaluate factors associated with smoking cessation attempts. BUT...the definition of cessation attempts described (transition from current smoker at index to former smoker at visit 2 or 3) does not align well with the construct of a quit attempt in which one tries or makes an effort to quit, but may not succeed. It is more aligned with that of \"recent quitting\" or \"smoking cessation\". The contrasting state isn't explicitly defined, but I assume it is current smoker at index who remains a current smoker at visits 2 and 3. Either the objective should be rephrased to be \"to evaluate factors associated with smoking cessation\" or you need a variable that better measures attempts. This confusion about what you actually measured and what you labelled it, comes up again and again as one reads the manuscript. In the discussion you state that the proportion of smoking patients who had a 'cessation attempt' was lower than observed in the general US population. Could that be because you measured something else and that whole paragraph is a comparison of apples to oranges?\n\n* In the study design section, it states that data were extracted on patients who had at least one recorded smoking status and at least three unique primary care encounters (between May2016 and May2019). This would suggest there are patients included that have only one smoking status measurement. Can this be correct? How does this allow for the determination of 'cessation attempts' as described? Do you actually treat no measure of smoking at a given visit as a non-smoker at that visit? Clearly, this whole measurement issue is not well described and potentially concerning. Of note, it is only at the end of the discussion section that a statement appears that the analysis excluded people with missing smoking status. This should be in the methods section and it should be explained more clearly.\n\n* Why does table 1 compare current smokers vs non-smokers at index? Why describe non-smokers at all? How is delivery of smoking cessation services and quitting smoking relevant to the population of patients who are non-smokers? Typically, table 1 provides baseline characteristics of the 2+ groups being compared. In this case, should that not be those who quit vs those who do not?\n\n* Your analysis of the characteristics of smokers with comorbidities is not connected to your stated study objective. The measures are all at index, it would seem and is not connected to your cessation (attempt) outcome. So, how does it fit in? Further, how were these groups defined? The only description is in the results section (I would have expected to find it in the methods), all it says is that based on EHR data, patients were stratified. Is each comorbidity group actually a dichotomous measure or is this one measure with mutually exclusive values? Does table 3 refer to all 11,622 patients who were current smokers at index? If so, is there a group of patients with no comorbidities?\n* Publons Reviewer Recognition. Springer Nature can send verification of this review directly to Publons (a subsidiary of Clarivate Analytics). If you would like to take advantage of this service, please click on the “Yes” option below. Your name, email address, title of the reviewed manuscript, name of the journal, and date of your review submission (the “Review Data”) will then be transmitted to Publons after the final decision on the manuscript has been made. If you have already registered at Publons, they will notify you of the receipt of this review and update your profile as per your settings and their policy. If you are not registered with Publons, you will receive an email from them asking you to register in order for them to be able to recognize your review on your new profile page. Publons may use the Review Data to generate derivative metadata for the benefit of Publons and you as a reviewer, carefully considering the sensitivity of such information. For example, Publons may verify your record as a reviewer by updating your profile published on its webservice if you have registered for such service or help editors to identify candidate reviewers. Please find the details of processing in Publons’ privacy policy https://publons.com/about/terms: **Yes**\n* Declaration of competing interests: **I declare that I have no competing interests.**\n* Reviewer Publication Consent. I agree for my report to be made available under an Open Access Creative Commons CC-BY License (http://creativecommons.org/licenses/by/4.0) if this manuscript is accepted for publication. Any comments that I do not wish to be included in the published report have been included as confidential comments to the editor, which will not be published.: **I agree to the terms of the CC-BY 4.0 license; please do not publish my name with my report. (default)**\n* Is the study design appropriate to answer the research question (including the use of appropriate controls), and are the conclusions supported by the evidence presented?: **No**\n* Are the methods sufficiently described to allow the study to be repeated?: **No**\n* Is the use of statistics and treatment of uncertainties appropriate?: **Yes**\n* Is the presentation of the work clear?: **No**\n* Are the images in this manuscript (including electrophoretic gels and blots) free from apparent manipulation?: **Yes**\n"},{"type":"editorInvitedReview","content":"","date":"2021-02-28T00:00:00+00:00","index":2,"fulltext":"Recommendation: Accept after minor essential revisions\nForm responses:\n---\n\nComments to Author:\n---\nThis is a generally well written manuscript addressing an important issue of what factors are associated with smoking cessation within a health care system. However, there are some areas where the manuscript could be improved.\nPlease provide more details of the safety-net clinics - are these free of costs to the clients or do clients make a co-payment for services?\nDo clients have to pay for smoking cessation counselling and do they have to pay for pharmacotherapy?\nIt is not clear to me if the data is confirmed receipt of the smoking cessation service (counselling and/or pharmacotherapy) or whether it is referral to these services.\nDid you analyse counselling + pharmacotherapy vs receipt of just one of these?\nAre there any measures of level of dependence that could be included as a co-variate?\nI think it also needs to be made clearer that when you are talking about quit attempts, you are talking about a successful quit attempt (change in status from smoking to not smoking between visits). You don't really know how many of these people who received counselling made a quit attempt and relapsed again between visits. Since most quit attempts fail within the first few days, I feel it's a bit misleading the way the data are presented. It suggests that those who received more support were less successful and this could relate to those needing more help having more difficulty/lower self-efficacy for quitting, rather than the assistance being a barrier to quitting.\nAlthough rates of basic cessation counseling were high, efforts were associated with lower odds of making a cessation attempt. - is this because the counselling is primarily being given as a motivational exercise to unmotivated clients rather than as counselling to support a quit attempt among those who indicate they would like to quit? Is there any information on what type of counselling (e.g. intended to motivate a quit attempt or intended to support a quit attempt among those already motivated)? Obviously it would be concerning if counselling was having a negative impact on quitting, but I suspect it is a reverse causation issue - those being perceived as requiring more help are being offered counselling. Considering that \u003e90% of clients were given smoking cessation counselling from the medical assistant, I wonder if you can really draw this conclusion - e.g. what is it about the minority that didn't get counselling that made them more likely to quit? Did they decline counselling because they were already accessing support elsewhere or had high self-efficacy?\nIt seems quite odd that pharmacotherapy is associated with lower rate of quitting compared to receiving no support - I feel like I need more information about what this entails, is it just a recommendation to use pharmacotherapy, was the patient dispensed pharmacotherapy and if so did they pay for it? Likewise with the counselling - are these referrals or actually recording of the person receiving counselling?\nIs there any quality assurance data to indicate how accurate and how complete the data on smoking status is for this dataset?\n\nThere is obviously an overlap in the different counselling types since it looks like nearly everyone receives counselling from the medical assistant and a high percentage also receive provider counselling. More details on what is considered counselling (e.g. would the medical assistant recording smoking status be considered 'counselling'?).\n\nMore discussion of the potential competing interpretations of the results are needed. e.g. are the results suggesting that counselling and pharmacotherapy lower quit rates?\n* Publons Reviewer Recognition. Springer Nature can send verification of this review directly to Publons (a subsidiary of Clarivate Analytics). If you would like to take advantage of this service, please click on the “Yes” option below. Your name, email address, title of the reviewed manuscript, name of the journal, and date of your review submission (the “Review Data”) will then be transmitted to Publons after the final decision on the manuscript has been made. If you have already registered at Publons, they will notify you of the receipt of this review and update your profile as per your settings and their policy. If you are not registered with Publons, you will receive an email from them asking you to register in order for them to be able to recognize your review on your new profile page. Publons may use the Review Data to generate derivative metadata for the benefit of Publons and you as a reviewer, carefully considering the sensitivity of such information. For example, Publons may verify your record as a reviewer by updating your profile published on its webservice if you have registered for such service or help editors to identify candidate reviewers. Please find the details of processing in Publons’ privacy policy https://publons.com/about/terms: **Yes**\n* Declaration of competing interests: **I have received funding from a public health unit to evaluate a health promotion program to increase use of smoking cessation services.**\n* Reviewer Publication Consent. I agree for my report to be made available under an Open Access Creative Commons CC-BY License (http://creativecommons.org/licenses/by/4.0) if this manuscript is accepted for publication. Any comments that I do not wish to be included in the published report have been included as confidential comments to the editor, which will not be published.: **I agree to the terms of the CC-BY 4.0 license; please do not publish my name with my report. (default)**\n* Is the study design appropriate to answer the research question (including the use of appropriate controls), and are the conclusions supported by the evidence presented?: **No**\n* Are the methods sufficiently described to allow the study to be repeated?: **Yes**\n* Is the use of statistics and treatment of uncertainties appropriate?: **Yes**\n* Is the presentation of the work clear?: **No**\n* Are the images in this manuscript (including electrophoretic gels and blots) free from apparent manipulation?: **Yes**\n"},{"type":"reviewerAgreed","content":"","date":"2021-02-09T01:00:00+00:00","index":2,"fulltext":""},{"type":"reviewerAgreed","content":"","date":"2021-02-09T00:00:00+00:00","index":1,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2020-12-28T00:00:00+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2020-11-22T00:00:00+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2020-11-21T23:00:00+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2020-11-21T23:00:00+00:00","index":"","fulltext":""},{"type":"submitted","content":"","date":"2020-11-11T00:00:00+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"609e74e3-b9b1-4688-b597-13e12cc2ab8a","owner":[],"postedDate":"December 1st, 2020","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":1226969,"name":"Health Economics \u0026 Outcomes Research"},{"id":1226970,"name":"Infectious Diseases"},{"id":1226971,"name":"Health Policy"}],"tags":[],"updatedAt":"2020-12-01T17:29:32+00:00","versionOfRecord":[],"versionCreatedAt":"2020-12-01 17:29:32","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-115150","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-115150","identity":"rs-115150","version":["v1"]},"buildId":"rHA-KDH7Qsr4HCuvH75dn","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}