Sean Nugent, BA

 Determine the viable yield of screening electronic Veterans Health Administration (VHA) records to identify patients who stop taking a long-term medication for reasons that might be addressed by healthcare providers.

 Prospectively screened cohort with mailed follow-up of positive screens.

 Electronic healthcare records were screened to identify patients receiving care in a Veterans Administration (VA) Health Care System who became past due for resupply of medication (statin) prescribed to reduce cholesterol and risks of adverse cardiovascular events. Subsequently, administrative data were used to classify and characterize patients as true or false positive screens. A follow-up survey mailed to the fi rst 1000 positive screens asked them if they were still taking a statin provided by the VHA, and if not, why?

From February to July 2010, 1000 (4.6%) of the statin-recipient cohort of 21,935 became past due for a resupply. Subsequently 824 (3.8%; 95% confi dence interval [CI] 3.5%-4.0%) were classified as true positives (positive predictive value 82%; 95% CI 80%-85%), and 176 (0.8%; 95% CI 0.7%-0.9%) as false positives. However, the 824 true positives included 95 deceased, 17 long-term care residents, 302 who reported good reasons for no longer getting the statin, and 208 who eventually got another supply. The overall yield of good candidates for efforts to reinstate long-term use of statins was only 20%.

The viable yield from electronically screening VA healthcare records to fi nd patients who stopped taking statins was low. More complete records and sophisticated screening programs are needed to improve the yield.

		 This attempt to screen electronic healthcare records to identify patients who appeared to stop taking a long-term medication identifi ed a large number of patients, but a low yield of viable candidates for efforts to restore use of the medication.

		 More sophisticated screening algorithms and complete healthcare records are needed to improve the actionable yield.

		 Each patient’s reasons for stopping a medication need to be ascertained to guide remedial actions.

Studies have consistently observed that many patients stop taking prescribed medications, thereby reducing the effectiveness of care for chronic conditions.

Investigators have associated numerous factors with discontinuation of medications; however, there are no prediction models that can accurately forecast when an individual will stop taking a prescribed medication or why.

 Integrated healthcare systems such as the Veterans Health Administration (VHA) may be able to use their electronic healthcare records to screen enrolled populations and identify patients as they appear to discontinue a medication.

 However, mass screening programs may compel healthcare systems to follow up a large number of positive screens. Thus, false positives and identifi cation of individuals that most likely would not be amenable to restoration of medication use must be minimized.

We developed and evaluated a screening program for VHA electronic health records. The primary objective was to identify patients as they stopped taking statin medications prescribed by Veterans Administration (VA) healthcare providers to reduce their cholesterol, hence the risk of adverse cardiovascular events including death. Specifi c aims were to estimate the proportions of true and false positive screens and the positive predictive value. A follow-up survey was mailed to positive screens to ascertain the reasons they stopped getting their statin prescriptions from the VHA and to help determine the percentage of positive screens that might be good candidates for remedial intervention.

The screening program was developed and tested in a VA healthcare system. Similar to other VA healthcare systems, the Veterans Health Information Systems and Technology Architecture (VistA) hierarchical files contain the electronic health records for a large hospital with outpatient clinics and several regional community-based outpatient clinics. We focused on continuing use of statins because they are commonly prescribed for long-term use, and lack of persistent use has been well documented in several healthcare systems.

 This evaluation and consent process was reviewed and approved by the local Committee for Human Subjects Research.

Files of VHA prescriptions (new, refill, partial fill) in VistA were screened biweekly beginning in February 2010 with 9 months of prior prescription records. Eligible individuals had received at least 2 separate 30- day supplies of a statin during the screening period to exclude dose adjustments and the impact of side effects that occur early in the course of treatment. A “past due date” was calculated as the date of release of the last statin supply plus the days of supply dispensed and any carryover from previous supplies in the fi le of statin prescriptions accumulated by the screening program. In addition, the calculated past due date was postponed by any inpatient days in the patient treatment file and a 120-day grace period. This grace period was based on past studies of medication persistence,

 and a separate (different data) preliminary examination of how much false positive screening rates increased as the length of the grace period was reduced from 180 to 30 days. Whenever the Minneapolis VistA records indicated a patient was past due for a statin resupply and there was no indication the recipient was deceased, VistA files maintained by other VA healthcare systems were searched to fi nd additional statin supplies, inpatient stays, and deaths. A positive screen was identifi ed whenever a patient’s calculated past-due date occurred before the screening date. Several descriptive variables were extracted from VistA files, including the type of prescriber (physician, other); whether the last supply was a new order or refill, delivered by mail, or on formulary; patient demographics; and address information.

A 3-page survey was mailed to the first 1000 positive screens that had a valid non-institutional postal address in the patient file. An introductory letter was sent, then the survey package, including a cover letter, preaddressed, postage-paid survey return envelope, and a $5 payment. A second survey packet without money was sent if no response was received within about 2 weeks. A third packet was sent via Federal Express or United Parcel Service after another 2 weeks without a response.

The survey instructed the recipient to look at the labels on their prescription containers, and asked whether they still had any of their statin which the VHA had most recently dispensed, and whether they were still taking any of that particular statin. If they were not taking the statin, the survey asked for the reason(s) they stopped it, using a 21-item checklist and an open-ended query. The survey asked everyone if they got their statin supply from any place other than a VHA pharmacy during the past 4 months, whether others (Medicare, Medicaid or state assistance program, employer, union or retirement insurance, military) helped them pay for their medicines, if they had stayed overnight in a non-VHA hospital or traveled away from home for a week or more during the past 4 months, and whether a doctor, nurse, or pharmacist had reduced their statin dose, or if they had done so on their own.

Nine months after the last survey was sent to positive screens, scrambled social security numbers of the 1000 positive screens were used to extract information from national VHA administrative records. The VA Decision Support System (DSS) Outpatient Pharmacy National Data Extract that is extracted from all VistA systems was used to determine whether each positive screen received any more supply of any statin from the VHA before their calculated past due date (a false positive screen) or did not (a true positive screen), and whether they received a VHA statin supply within 6 months after their past due date that would be consistent with a temporary gap in supply.

Baseline variables representing the year prior to the last statin supply dispensed before the patient screened positive were extracted from several sources. Prescription variables from the DSS National Data Extract included the total number of prescription fills, number of statin fills, and whether each subject received a prescription in each of 32 different classes of medications, including those used for atherosclerotic diseases (ischemic heart disease, etc) or risk factors (blood pressure, diabetes, etc) and depression. The VHA Medical SAS data files compiled by Austin Information Technology Center provided national information on VHA outpatient encounters and inpatient stays. All diagnoses recorded during the baseline year were used to identify chronic comorbidities based on an enhanced Elixhauser classification scheme.

 In addition, categories for ischemic heart disease (ICD-9-CM codes 410 through 414), heart failure (398.91, 402.01, 402.11, 402.91, 404.03, 404.11, 404.13, 404.91, 404.93, 425.4, 425.7-9, 428), and dementia (290.1-4, 290.9, 291.1-2, 294.1, 331.0-2, 331.82) were created. Monthly enrollment records were matched to the date of each subject’s last VHA statin supply to determine the duration of VHA enrollment; enrollment priority (1-8), which, in part, determines whether there was a VHA prescription copayment; and other active health insurance including Medicaid. Dates of death were obtained retroactively from monthly enrollment records.

Descriptive statistics, mean ± standard deviation, median (25th-75th quartiles), and proportion (percentage) as appropriate for the level of measurement and distribution of the data are reported. Binomial 95% confi dence intervals are estimated for proportions. We did not collect follow-up data on patients who did not become positive screens, thus the sensitivity, specificity, and negative predictive value could not be estimated. Group characteristics were compared by Student t test for normally distributed continuous variables, Wilcoxon rank-sum test for skewed continuous measures, or Pearson’s 

 values are not adjusted for multiple comparisons. Stata software (version 10.1) was used for all analyses.

Within 5 months of beginning to screen the statin-recipient cohort approximately every 2 weeks, 1000 patients (4.6%; 95% confi dence interval [CI] 4.3%-4.8%) out of 21,935 became past due for a resupply of statin medication, ie, became a positive screen. Their statin prescriptions are characterized in 

. Consistent with the 120-day grace period and the predominant dispensing of a 90- day supply, the positive screens became past due 214 ± 23 days after their last statin supply was sent out. The positive screens received 3.1 ± 1.7 statin supplies and 249 ± 92 days of supply during the baseline year. Thus, the majority appeared to have stopped after receiving more than the required minimum of 2 statin supplies. Subsequently 824 in the statin user cohort of 21,935 (3.8%; 95% CI 3.5%-4.0%) were judged to be true positives based on not getting a resupply of any statin medication from the VHA for at least 4 months after their past supplies should have been consumed. The estimated positive predictive value was 824 out of 1000, or 82% (95% CI 80%-85%). During follow-up we discovered that 11% of the 824 true positives had died (n = 95) before their past-due date, or became nursing home residents (n = 17), where most likely they would not be personally responsible for taking or discontinuing the statin. Later DSS prescription extracts found 176 (0.8%; 95% CI 0.7%-0.9%) had received another supply before their calculated past-due date and therefore were false positives. The statin supplies defi ning false positive screens were all dispensed in February 2010, the first month of screening the prescription records. Characteristics of the false and true positives are compared in 

. There were no major differences in these or any other comorbidities or medication classes that are not shown, or variables listed in Table 1.

The follow-up survey was sent within 18 ± 10 days after the calculated past due date. Excluding the 95 deceased, 

 nursing home residents, and 21 cases with undeliverable addresses, the response rate was 786 out of 867 (91%), including 640 true and 146 false positives. Reasons reported by the true positives for no longer taking the statin provided by the VHA are listed in 

. Of the 640 true positives, 302 reported at least 1 reason that could be diffi cult for healthcare providers to address, including physician orders, side effects, switching to alternative treatments, or using non-VHA sources of supply. Other reasons that were given might be more amenable to efforts to reinstate long-term use of a statin. However, 208 of the remaining true positives that gave reasons that might be more amenable to reinstating use received another statin supply from the VHA within 6 months after becoming past due. Thus, efforts to reinstate their statin would not be required. The 

 summari zes the overall yield of good candidates for efforts by healthcare providers to reinstate long-term statin use.

The majority of patients identifi ed by this particular approach to screening electronic healthcare records were questionable candidates for reinstatement of long-term statin use. The yield could be improved in several ways. Having more up-to-date records of deaths and transitions into long-term institutional care would eliminate some that definitely did not require follow-up. A more advanced screening program that could determine whether VHA providers had discontinued the statin would be very helpful. Most likely this would require a search of textual notes. Searching for side effects such as elevated liver enzymes or a diagnosis of myopathy could exclude those with adverse effects that could have prompted discontinuation of a statin. However, some adverse effects, such as patients not liking how they felt on medication, will not be recorded or may be recorded as textual notes. Likewise, information on alternative treatments might not be readily accessible to an electronic screening program. Searching the healthcare record for cholesterol levels indicating adequate cholesterol control after patients became past due could further reduce the large number of positive screens needing follow-up.

A substantial proportion of the positive screens obtained statin supplies outside of the VHA. Lower out-of-pocket costs provided by insurance coverage or generic supplies provided by discount pharmacies increasingly are reducing the VHA’s ability to rely solely on internal prescription records. Efforts to reconcile medications could alleviate this problem, if reconciliation becomes routine during all patient encounters and the information is readily retrievable.

 Perhaps the reasons patients are no longer taking previously prescribed medications could be ascertained and recorded as well.

Another way to reduce the burden of follow-up of positive screens would be to increase the grace period and thereby exclude patients who eventually obtain another supply of the medication. Thirty-seven percent of the positive screens reported they were still taking the statin they got from the VHA, and 76% of them did get another statin supply within 6 months of their calculated past-due date. Although the follow-up survey may have prompted some to request another supply of statin, the percentage that eventually obtained another statin supply is similar to our preliminary data wherein positive screens did not receive a follow-up survey. Others have noted the dynamic nature of patients’ prescription supplies.

 Other analyses not presented herein suggested that most of the patients that reported they were still taking the statin despite being past due for a refill relied solely on the VHA for this prescription medication and may have accumulated suffi cient supplies to bridge a 4-month gap. Thus, the screening program may have given the wrong impression in these cases. However, accumulation of excess supply could be due to unrecognized self-made or provider-directed dosage reductions. Another study of veterans with hypertension reported nearly 50% disagreement between a 90-day measure of insuffi cient medication supply and a self-reported measure of nonadherence.

 These results along with ours indicate that more information is needed before acting on observed gaps in medication supplies. Conversely, only 22% of those that confi rmed they were no longer taking the VHA statin received another statin supply within 6 months, confi rming the veracity of this type of self-report.

Asking positive screens whether or not they stopped taking a medication provided by the VHA and if they stopped, why, is a reasonable approach to try to reinstate use of a medication.

 The survey respondents gave many reasons for stopping their statin that typically would not be noted in medical records, and therefore would need to be elicited by some sort of follow-up with the patient. Although not responding to a survey about medication beliefs has been associated with worse adherence, the response to our follow-up survey was 91% for both true and false positive screens.

However, the fi rst survey package included a $5 payment and as many as 3 survey packages had to be sent to get a response. This approach may not be practical when there are a large number of positive screens. The effectiveness of a simple refi ll reminder sent to all positive screens may be limited if the reminder does not address the patients’ reasons for stopping the medication, and may enrage those who receive an inappropriate reminder.

This attempt to screen electronic records produced a substantial number of false positives. We were not able to determine why the screening program did not incorporate statin prescriptions that were later found in prescription records extracted from VistA files. None of the comparisons of true versus false positive screens provided much insight into this problem. It is somewhat encouraging that the false positives were limited to prescriptions dispensed during the fi rst month of the screening program when there might have been an unrecognized problem while initiating the screening process. However, if timely transfer of prescription-filling records from the mailorder pharmacy to the local VistA fi les was responsible, false positives could be a recurrent problem.

This attempt to use the VHA electronic health records to monitor continued use of a medication prescribed for a chronic condition is limited to a single class of medications in a single VA Health Care system. However, the electronic health records are used by all VA Health Care systems, and the likelihood of, and reasons for, discontinuing statins are similar to several other long-term medications.

Thus, insights from this evaluation could apply more generally and help guide further development of electronic screening programs. By design, we did not begin to screen patients until they had received at least 2 statin supplies to try to avoid any effects of trial doses, dose titration, early intolerance, and patients that didn’t intend to take the medication that was prescribed. We were most interested in screening patients that at least initially appeared to be taking and tolerating the medication. Beginning to screen patients after they fi ll their fi rst prescription may alter the results reported herein. Not all of the positive screens returned the survey or indicated whether or not they were still taking the statin, making it diffi cult to estimate the overall percentages in the population that stopped statin use for each reason. The viable yield of the screening program could be overestimated for the same reason. Nevertheless, the list of reasons and number of times they were cited provides important insights into longterm statin use and what issues need to be ascertained and addressed by this type of mass screening program. For example, healthcare providers may have to convince some patients that the effort and cost of long-term statin use is worthwhile and make sure to show them how much their cholesterol and risk has been reduced. Other patients need help managing their medications and out-of-pocket costs. Healthcare providers might not be able to resolve some of the reasons patients gave for discontinuing a statin.

In conclusion, the actionable yield of this particular effort to screen the VHA’s electronic healthcare records of patients that were filling prescriptions for a long-term medication was quite low, due to information that was not placed in the electronic records in a timely manner or that would be diffi cult to extract without a much more sophisticated program including text searches. Furthermore, supplemental information is necessary to determine the patients’ undocumented reasons for discontinuing a medication. Mass screening of electronic health records followed by a large number of mailed follow-up surveys may not be an effi cient method for identifying viable

candidates for interventions to reinstate long-term medication use.

 Allocation of limited healthcare resources to this type of mass screening program would have to be justified by the magnitude of improvement in patient outcomes that would depend on improving the viable yield as well as whether healthcare providers could satisfactorily address the variety of reasons patients have for discontinuing a medication.

The excellent work of Andrea Cutting, MA, who compiled the survey data and extracted and processed the data from VHA administrative records, and Hannah Fairman, BS, who managed the follow-up survey, is gratefully acknowledged.

Articles

Many patients stop taking medications for chronic conditions, thereby reducing the effectiveness of healthcare. An attempt to screen electronic VA healthcare records to identify patients as they discontinued a medication was not an efficient approach to this problem.

Screening Electronic Veterans' Health Records for Medication Discontinuation

To test whether an interactive voice response (IVR) system phone call was equally effective as a nurse-delivered phone call at educating and preparing patients for flexible sigmoidoscopy (FS) and colonoscopy examinations.

The trial included patients with upcoming FS or colonoscopy appointments to test the equivalence of an IVR system to nurse-delivered phone calls in reducing appointment nonattendance and inadequate preparation for an examination. Message timing and satisfaction with the intervention were assessed. The 3 study conditions included the following: nurse phone call 7 days before the procedure, IVR system call 7 days before the procedure, and IVR system call 3 days before the procedure. All calls included an appointment reminder, information about preparation for the examination, and encouragement to prepare for and attend the examination.

 A total of 3610 patients were eligible for the study; of these, 1229 (34%) were scheduled for FS and 2381 (66%) for colonoscopy. There were no statistically significant differences across the 3 study arms in appointment attendance or adherence to preparation instructions. Significantly more patients in IVR conditions reported neutral perceptions about the phone calls, and more patients receiving nurse calls reported very positive perceptions about the phone calls.

An IVR system call is as effective as a nurse phone call for ensuring that patients attend appointments and are adequately prepared for endoscopy examinations.

	An interactive voice response (IVR) system, an automated phone—based technology that allows for 2-way communication between clinics and patients, was used to remind patients of upcoming flexible sigmoidoscopy and colonoscopy appointments and to provide them with information about how to prepare for the examination. Our findings include the following:

		The IVR system was effective at reminding patients of their appointments.

		An IVR system can effectively deliver complex information, such as preparation information.

		An IVR system is equally effective as phone calls from clinic nurses at delivering information.

		Patients receiving IVR messages reported more “neutral” perceptions about the phone calls; patients receiving nurse calls reported more “very positive” perceptions about the phone calls.

	Colorectal cancer (CRC) is the third leading cause of cancer deaths in the United States.

 Colonoscopy and flexible sigmoidoscopy (FS) are used to detect precancerous and cancerous polyps in the colon and rectum. Recent evidence-based screening guidelines prioritize these tests over other screening modalities because they can prevent CRC rather than simply detect it early.

	National CRC screening rates remain low compared with frequencies of other cancer screening tests.

 Likewise, completion rates of follow-up diagnostic tests after a positive screening test result are low. In a study

 conducted through the Veterans Health Administration, where screening rates are typically higher than in other settings, only 44% of those with a positive fecal occult blood test result completed a diagnostic colonoscopy within 12 months. Even when appointments are scheduled, screening completion rates are low, with a study

 finding that only 44% of those scheduled for a colonoscopy and 59% of those scheduled for an FS completed their examination. Clinicians, researchers, and patients agree that preparation for endoscopy examinations is complex and can be difficult but needs to be performed properly to complete the examination.

 In addition to finding transportation home after the procedure, other barriers to endoscopy that reduce attendance and completion rates include the following: fear of positive findings

; discomfort, embarrassment, and anxiety about the procedure

; and worries and concerns about the procedure and an absence of symptoms.

 Studies have found that patient education

 significantly reduce  appointment cancellations. Other researchers have found that education can lead to higher endoscopy completion rates, especially when information is delivered multiple times and in understandable formats.

	Using innovative cost-effective approaches for improving FS and colonoscopy completion rates might benefit patients by assuring essential and timely care that could reduce morbidity and mortality. A novel approach used in other healthcare settings is an interactive voice response (IVR) system, an automated phone—based technology that allowsfor  2-way communication between clinics and patients. Interactive voice response is a mode of message delivery that has been shown to be effective in medical contexts.

 It allows for calls to be delivered at any appointed time and for information to be repeated, both of which increase the likelihood that the call will be delivered and received. For example, phone appointment reminders delivered via IVR have been shown to increase appointment adherence and preappointment procedure completion

 and to decrease appointment nonattendance.

 have used IVR to deliver educational messages.

 we developed an IVR system that provided patients with cues to action for appointment attendance and procedure preparation, including targeted educational information about susceptibility and severity of CRC and motivational messages that addressed risks, benefits, barriers, and self-efficacy associated with preparation and procedures. Our objectives were to assess the following: (1) the equivalence of theory-based phone messages and education provided by an IVR system and by nurse-delivered calls (NDCs) in promoting appointment attendance and adherence to preparation instructions for FS and colonoscopy, (2) the effect of the timing of IVR messages delivered 3 days versus 7 days before the scheduled appointment, and (3) any differences in patient satisfaction between IVR messages and NDCs.

	We conducted a 3-arm randomized controlled trial among patients with upcoming FS or colonoscopy appointments scheduled in 2 gastrointestinal (GI) endoscopic procedure clinics at the Minneapolis Veterans Affairs Medical Center. Patients included those being screened and those having follow-up appointments after receipt of abnormal test results. While these clinics provide both upper and lower GI examinations, we included only the 2 lower GI examinations (FS and colonoscopy) because they comprise 67% of the endoscopic workload and because 69% of all cancellations in these clinics are for these 2 procedures.

	During the study period, an automated program was applied each night to the hospital computerized record system to identify new appointments made in the clinics. Patients scheduled for an appointment were evaluated for study eligibility criteria (described herein). Those eligible were randomized to 1 of 3 study arms, with the randomization stratified by procedure type. Clinic procedure nurses and physicians were blinded to the randomized conditions. Study arms included the following: (1) NDC (phone call from a nurse 7 days before the procedure), (2) IVR7 (phone call from the IVR system 7 days before the procedure), and (3) IVR3 (phone call from the IVR system 3 days before the procedure). The NDC was the clinic’s usual care procedure. For the intervention to be unobtrusive to clinic staff and to retain blinding to the intervention condition, an NDC at 3 days before the procedure was not included. Intervention calls were initiated from a Minneapolis Veterans Affairs Medical Center computer server running a software program (AudioCARE; AudioCARE Systems, Berwyn, PA), and NDCs were initiated by nurses in the recovery room of the GI clinic.

	Appointment and GI procedure data, including patient appointment records and procedure notes for the initial scheduled appointment, were extracted from medical records to assess study outcomes for all condition arms. Additional patient health information was extracted, including diagnoses of chronic physical and mental health conditions in the year before the index appointment.

	One week following the initial appointment, patients were sent a questionnaire about their experiences with the reminder system and the scheduled procedure. The questionnaire asked patients if they recalled a preappointment phone call, the type of call (whether an IVR message or an NDC was received), and (if they received the call) their opinion about the thoroughness of preparation instructions. If they prepared for and attended their appointment, they were also queried about their experiences with the preparation and procedure and any perceived barriers and facilitators to appointment attendance and procedure preparation. Questionnaires were mailed directly to participants’ homes by study staff.

	All patients scheduling an FS or a colonoscopy appointment between August 20, 2007, and October 31, 2008, in either clinic were assessed for study inclusion. Patients were ineligible for the study if, based on a medical record review before randomization, they lived in a nursing or group home or a homeless shelter, had no listed phone number, scheduled the appointment less than 8 days in advance, had multiple GI procedures scheduled for the same day (such as patients with both upper and lower GI procedures), or had a diagnosis of type 1 diabetes mellitus, dementia, or Alzheimer’s disease. These patients had unreliable means of receiving the intervention (eg, no phone), or the IVR system would have provided inappropriate or inaccurate information (eg, based on their health conditions).

 More than 95% of the FS and colonoscopy appointments were scheduled within 40 days before the appointment; 85% were scheduled 20 to 40 days before the appointment. The clinic protocol was to mail all patients their appointment information, preparation instructions, and preparation materials within 1 week after their appointment was made. For patients in the NDC arm, a recovery room nurse attempted to call to remind patients of the appointment and review preparation instructions 7 days before the appointment. Nurses used computerized templates to guide them through the call. The templates, one for FS and another for colonoscopy, included logistical information about the appointment (such as time, place, what to bring, and whether the patient needed someone to drive him or her home) and preparation instructions (such as what medications to stop and how and when to take the prescribed colon cleansing laxatives). Nurses also answered any questions during the call. An NDC was considered complete if a nurse spoke  directly with the patient. Per clinic protocol, nurses did not leave any message if a patient was not reached, but made 1 more attempt the following afternoon. If that call was unsuccessful, no message was delivered.

	Patients in the IVR study arms (IVR7 and IVR3) were mailed appointment information and preparation instructions and materials identical to those mailed in the NDC arm. Instead of NDCs, they received appointment reminder information and preparation instructions via an IVR system.

	There is no evidence base concerning effective time frames for prompting patients. We selected a 7-day window based on current NDC practices and protocol and a 3-day window because this was the day before patients were to start the colon cleansing preparation, and we hypothesized that the immediacy of the message might be a cue to action. While the IVR system allows for calls to be delivered at any time, patient preferences for the time of day to be called for the NDC were unknown. Phone calls were programmed to start in the morning. If an answering machine picked up on the initial call, the IVR system left a general message about the purpose of the call. The system was programmed to call again in the afternoon and then again in the evening until the patient answered. Messages were left only on the first attempt. If the IVR call was not completed that day, the process was repeated the following day. Patients who answered the call had the option to have the system call back at a later time. An IVR call was considered complete if the patient answered and confirmed his or her appointment.

	The IVR system allowed patients to verify and confirm their appointment, respond to instructions about logistics, request additional preparation materials, answer queries about their current health, listen to preparation instructions, have any information repeated, ask for a summary of instructions, or leave a message for a nurse who would call back within 24 hours. Embedded in these messages was the educational information about susceptibility and severity of CRC, as well as motivational messages that addressed risks, benefits, barriers, and self-efficacy associated with preparation and procedures. At any time during the call, the patient could request to be transferred to the clinic to leave a message for a nurse.

To assess whether the intervention was delivered in a uniform way, we collected details about the appointments and then calculated the proportion of attempted and completed calls for each study condition. Attempted and completed IVR calls were collected via the IVR system, while attempted and completed NDCs were recorded on the computerized nurse template. For the IVR system, the proportion of requests to be transferred to the clinic was also assessed and compared across IVR arms. The mean number of attempts made before reaching the patient was also calculated, and the total time spent on the IVR call was recorded. For NDCs, these data were unavailable; however, for the number of attempted calls, the nurse protocol was to make a maximum of 2 attempts to reach each patient.

 All randomized patients were mailed a questionnaire regardless of appointment attendance. The questionnaire was based on health belief model

 constructs and assessed attitudes and beliefs about preparing for and completing an endoscopy examination. Sent with an introduction letter, postage-paid return envelope, and $5 unconditional cash incentive, the 12-page survey included questions about perceived disease susceptibility and severity and about benefits, barriers, and self-efficacy relative to completing an endoscopy examination. We also included questions about satisfaction with the reminder phone calls and about general patient demographics, including age, employment status, race/ethnicity, education, income, and marital status. Using a modification of the method by Dillman et al,35 we mailed a reminder postcard 14 days after the survey was sent. A second survey packet without the incentive was sent to nonresponders 21 days after the initial mailing. Study staff phoned patients 28 days after the initial mailing to remind them about the survey but did not attempt to administer the questionnaire over the phone.

	We hypothesized that equal proportions of patients in the NDC and IVR conditions (1) would not attend their scheduled FS or colonoscopy appointment and (2) would not have thorough bowel preparation for the scheduled FS or colonoscopy. Likewise, we hypothesized that equivalent proportions of patients would be nonadherent to appointment attendance and preparation instructions when the intervention message was delivered 3 days versus 7 days before the index appointment date. Finally, we hypothesized that patients in the NDC arm would have a more positive perception of the calls than those in the IVR arms.

	The standard equivalence test (at a .015 significance level) examines whether a 97% 2-sided confidence interval for a difference in outcome rates falls within a prespecified equivalence boundary. Because no literature specifies the clinically significant difference in completion rates, we relied on clinician experts to specify the equivalence boundary. Four practicing gastroenterologists were queried about their perceptions of equivalence. The unanimous opinion was that at least a 10% difference in completion rates would be clinically significant and that anything less than 10% would be considered equivalent.

	Clinic data from a 6-month period before the study (December 2006 through May 2007) showed appointment completion rates of 67% and 62% for FS and colonoscopy, respectively. Using an equivalence boundary of 0.10, a sample size of 743 subjects per group provided 90% power for the study with a level of .05 divided by 3 and an underlying 65% baseline completion rate. We initially planned to obtain 743 patients in each study arm for each procedure, but changes in clinic practices after the study planning prohibited recruitment of sufficient numbers of FS cases.

	The study evaluated the following 2 main outcomes: appointment nonattendance and preparation nonadherence. Nonattendance was defined as canceling the appointment or not attending the appointment. We did not consider appointments canceled by the clinic as nonattendance. Preparation nonadherence assessed whether patients had adequately prepared to complete the procedure. We used procedure notes to determine if the patient was adequately prepared or if the physician was unable to evaluate the quality of the preparation. Four participants arrived for their appointment but were then deemed inappropriate for the procedure because of serious complicating health conditions. These 4 patients were excluded from analyses.

	Perceptions of preappointment phone calls were measured using a survey question about how satisfied respondents were with the reminder phone calls. Response options were “very positive,” “positive,” “neutral,” “negative,” or “very negative.”

	All hypotheses were tested separately for FS and colonoscopy using intent-to-treat analysis. To test whether outcomes for NDCs and IVR messages were equivalent and whether the 2 IVR interventions were equivalent, we constructed 2-sided 97% confidence intervals for the difference in completion rates, with the confidence level chosen as a Bonferroni multiple comparisons adjustment for the main comparisons. If the confidence interval fell within −10% and 10%, the conditions were considered equivalent.

	To address potential survey response bias, we estimated response propensities using administrative measures available for all subjects, stratified the sample into strata with estimated propensities within .05 of one another, and verified the balance of the covariate measures between respondents and nonrespondents within strata. We estimated treatment differences using weighted means of within-stratum differences, weighting by the sample proportions of the strata. Likelihood ratio tests comparing a generalized linear model using only stratum as a predictor and a model using stratum, treatment, and their interaction were used to test for differences.

	To assess whether the intervention was implemented uniformly across patients in the intervention arms, appointment details (including distance traveled to the appointment, appointment day and time, and proportion of attempted and completed calls) were compared across intervention arms.

 show the flow of participants for the intervention and survey using Consolidated Standards of Reporting Trials guidelines.

 A total of 3610 patients were eligible for the study. Of these, 1229 (34%) were scheduled for FS and 2381 (66%) for colonoscopy. Approximately 36% of all procedures were for screening, while the others were for diagnosis or surveillance.

An interactive voice response system is as effective as nurse phone calls for ensuring that patients attend appointments and are adequately prepared for endoscopy examinations.

Improving Endoscopy Completion: Effectiveness of an Interactive Voice Response System

Promotion of repeat tobacco dependence treatment among relapsed smokers interested in "recycling" (repeat quit attempt) may be a promising approach to increase quit rates.

To report relapsed smokers' interest in recycling and their treatment preferences.

Descriptive analysis of a population of relapsed smokers who were randomized to receive a recycling intervention strategy to increase tobacco dependence treatment rates, as part of a randomized controlled trial at 5 Veterans Affairs medical centers.

Individuals prescribed a tobacco dependence medication in 2002 were eligible and were identified from the Department of Veterans Affairs Pharmacy Benefits Management database. Intervention group participants (n = 951) were contacted for a standardized telephone interview approximately 6 months after the prescription fill date to assess smoking status, interest in recycling, and treatment preferences. Bivariate analyses and generalized linear mixed-model regressions were used to describe outcomes.

The response rate to the intervention telephone call was 62% (586/951), at which 61% (357/586) of respondents had relapsed. Almost two thirds of relapsed smokers were interested in recycling within 30 days. Of these, 91% wanted behavioral or pharmacologic smoking cessation treatment, and 64% wanted behavioral and pharmacologic treatment. In multivariate analyses, independent predictors of interest in recycling within 30 days included black race, lower smoking level, and greater number of smoking-related medical conditions.

Most smokers who attempt to quit but relapse want to quit again right away, and most are interested in receiving behavioral and pharmacologic treatment.

The prevalence of tobacco use in the United States cannot be reduced dramatically without helping smokers quit smoking.

Successful cessation has immediate health benefits for all smokers and has a major effect on the economic burden of tobacco-related diseases.

On the one hand, interest in quitting is high, and 70% of the 46 million smokers in the United States want to quit completely.

On the other hand, smoking cessation rates for the past 10 to 15 years have remained stable, despite the increased availability of effective behavioral and pharmacologic treatment to help smokers quit smoking. For example, the quit ratio (proportion of ever smokers who have quit), a "dipstick" measure of successful quitting, has remained roughly 50% since 1990.

In addition, most quit attempts, including those with treatment, do not result in long-term cessation. Depending on the population and intensity of treatment, 65% to 95% of quit attempts end in failure.

 This high rate of relapse illustrates the difficulty and challenges of achieving and sustaining successful smoking cessation.

Promoting repeat tobacco dependence treatment among relapsed smokers interested in "recycling" (repeat quit attempt) may be a promising approach to re-engage smokers in the quitting process and to increase cessation rates. Recycling and promotion of repeat treatment recognizes that tobacco dependence is a chronic relapsing disorder and that most smokers will make multiple attempts to quit before successfully achieving long-term abstinence. Because quit attempts aided by behavioral or pharmacologic treatment for tobacco dependence are more likely to be successful than untreated quit attempts,

it is important that recycling strategies involve delivery of additional treatment. However, clinicians may fail to make tobacco cessation a high priority because they believe that their patients are uninterested in treatment.

Indeed, there is limited information about relapsed smokers' interest in recycling after a failed quit attempt or about their preferences for various treatment methods.

The Recycling Smokers Through Effective Treatment (RESET) study is a multicenter randomized controlled trial to test the effectiveness of a recycling intervention strategy designed to increase rates of tobacco dependence treatment. The analyses presented herein describe the prevalence and predictors of interest in recycling and smoking cessation treatment preferences among participants in the intervention arm of the RESET study.

Eligible participants were identified from the Department of Veterans Affairs (VA) comprehensive database on VA-issued pharmaceutical prescriptions (Pharmacy Benefits Management database), which includes medication utilization information for every VA patient who has activity at a VA pharmacy. Individuals 19 years or older who received a prescription for nicotine replacement therapy (NRT) (nicotine inhaler, spray, patch, or gum) or for bupropion sustained release (SR) for smoking cessation between February and October 2002 from 1 of 5 participating VA medical centers (Denver, Colo; Salt Lake City, Utah; New Orleans, La; Providence, RI; and Puget Sound, Wash) were eligible for the study. The selected facilities were chosen to represent variation in size, region, and tobacco-dependence prescription protocols (restricted vs unrestricted prescribing privileges for primary care providers). Individuals who did not have a primary care appointment at a participating facility in 2001 or 2002 were excluded, as the intervention was directed in part toward participants' primary care providers. Individuals who did not have complete telephone numbers and addresses in local computerized patient records systems were also excluded because participants in the intervention and control groups were contacted by letter and telephone. The institutional review board at each participating site approved this study.

In the RESET study, randomization occurred at the level of the participant. The number of participants recruited from each site was proportional to its tobacco dependence prescription volume during the recruitment period. Between 245 and 500 participants were recruited from each site; the combined sample size for all facilities was 1900. Using a computer-generated algorithm, the 1900 eligible veterans, stratified by facility, were randomly assigned to (1) a patient telephone call and computerized provider prompt (intervention group) or (2) usual care (control group). The intervention strategy included a telephone call to collect information about smoking status, interest in recycling, and treatment preferences. The information collected was communicated through an electronic progress note to the primary care provider and, if requested by the VA facility, to a representative of the facility's smoking cessation clinic. Progress notes were sent for abstinent patients and for patients interested in quitting but not for patients who wanted to quit later or not at all. The following 4 types of progress notes were generated for use in the intervention: (1) the patient wanted to quit using NRT, (2) the patient wanted to quit using bupropion SR and reported potential contraindications to its use, (3) the patient wanted to quit using bupropion SR and reported no potential contraindications to its use, or (4) the patient successfully quit.

Participants randomized to the intervention group were contacted by telephone approximately 6 months after they received a prescription for NRT or for bupropion SR for smoking cessation. Trained telephone interviewers at the coordinating site (Minneapolis VA Medical Center) made 6 attempts to reach each participant. During the intervention telephone call (5-10 minutes), interviewers determined participants' smoking status, interest in making a repeat quit attempt, and treatment preferences (eg, specific behavioral and pharmacologic treatment options). Participants in the control group were not contacted by the study team during the intervention period. All RESET study participants (intervention and control) were contacted 12 months after they received their index prescription for a brief follow-up telephone survey to assess demographic characteristics, which are used as covariates in the analyses herein and as outcome measures for the larger trial.

The objective of the intervention telephone call was to gather information and to assess interest in quitting. We did not attempt to persuade participants to quit smoking. Smoking status was assessed, and for those who were smoking, we assessed the mean number of cigarettes they smoked per day and their interest in making another quit attempt. For those participants interested in trying to quit again, we assessed their preferences for behavioral counseling or for pharmacologic treatment for a repeat quit attempt. For smokers interested in quitting, we also collected information about the medications used in their last quit attempt, adverse effects associated with the last medications used, and quit challenges. When participants were asked about their interest in quitting again, they received no encouragement to quit. For participants who were not interested in quitting, no attempt was made to change their minds. For participants who expressed interest in quitting but stated that they were not interested in the available behavioral or pharmacologic treatment options, we reminded them that their chances of success were much greater if they used counseling and medication therapy and gave them another opportunity to choose a treatment option. For patients who refused this second opportunity, no additional attempts were made to encourage them to choose a treatment option.

Intervention participants were asked if they were using medications for smoking cessation. Participants who were using a smoking cessation medication were considered still in treatment, and their smoking status was not assessed. To assess smoking status, all other participants were asked whether they had smoked any cigarettes within the past 7 days. If they reported that they had not smoked any cigarettes, including even a puff, they were considered abstinent. If they reported any smoking in the past 7 days, they were considered to have relapsed to smoking.

The intervention telephone call collected self-reported information about quitting history, use of smoking cessation medications, experience with adverse effects, quit challenges, and current number of cigarettes smoked per day (measure of nicotine dependence). To assess the presence of smoking-related or mental health comorbid conditions, we used participants' clinical diagnosis information in the 2 years before study enrollment from VA outpatient medical records.

We obtained information about age and sex from VA outpatient medical records and information about race, education, and marital status from the 12-month follow-up survey. In addition, information about the characteristics and practices of the smoking cessation programs (eg, medications available, prescribing policy, and behavioral counseling options) available at each participating facility was provided by the facility's smoking cessation coordinator during a brief telephone survey.

The primary outcomes for this analysis were participants' interest in recycling within 30 days and their treatment preferences. To assess interest in recycling, participants were asked if they were seriously thinking about quitting smoking within 30 days. To assess treatment preferences, participants were read a brief description of each of the smoking cessation medication options available at their site. The nicotine patch and bupropion SR were available at all 5 sites; 4 of the sites also offered nicotine gum. Because the nicotine inhaler and the nicotine nasal spray were not available at most of the participating sites, they were not offered as treatment options. Participants interested in recycling were asked to choose which option they would be most interested in trying during their next quit attempt. They were allowed to pick more than 1 option if they preferred. Participants were also read a brief description of the behavioral counseling options available at their site and could choose an option if they wished. Three of the 5 sites offered group and individual counseling. One site only offered participants the option of developing a quit plan with their primary care provider. At another site, participants did not have the option of attending individual counseling sessions (although group sessions were available).

To assess predictors of interest in recycling within 30 days, we used bivariate tests to compare characteristics of relapsed smokers who were ready to make a repeat quit attempt within 30 days with characteristics of those who were not. To examine independent predictors of recycling within 30 days, we used generalized linear mixed-model regression analyses.

To account for clustering of participants within facility, we included VA facility as a random effect. Missing values for race (n = 7), number of cigarettes smoked per day (n = 9), and education (n = 38) were imputed using a standard multiple imputation method.

This method derives imputed values from a regression model using completed (nonmissing) measures as predictors. In addition, we repeated the regressions without imputations, which produced similar findings. Backward elimination was performed to obtain the final model, using the following criteria: At each step, the largest nonsignificant (

>.10) fixed effect was eliminated, which was determined by the type III F test results, as long as no level of the factor in comparison with the reference group was significant at the 5% level.

Elimination continued until all the remaining fixed effects were significant or until at least 1 comparison with the reference group was significant. The Hosmer-Lemeshow goodness-of-fit test was used to assess the model fit.

To further investigate institutional factors that might influence interest in recycling, we conducted bivariate analyses between the facility random-effects coefficients from the final model and the facility variables from VA administrative sources and the smoking cessation coordinators' survey. We looked for potential trends by assessing Pearson product moment correlation coefficients if the facility variable was continuous and by using Cochran-Armitage trend test statistic values if the facility variable was dichotomous. We only report the facility variables that illustrated at least a marginally significant result (

In the RESET study, 951 participants were randomized to receive the recycling intervention strategy (

). Intervention participants were telephoned on average 6 months (range, 4-8 months) after the fill date of the index prescription, and 586 participants were reached and consented for the telephone interview (62% response rate). At the intervention telephone call, 61% (357/586) of respondents had relapsed to smoking. However, 18% of respondents were abstinent from smoking, and 21% were in the process of quitting and were receiving smoking cessation treatment.

We compared characteristics of relapsed smokers with those of respondents who were abstinent. There were no significant differences in demographic characteristics, except that relapsed smokers on average were younger (

). Also, the mean time to the intervention telephone call was longer for respondents who had relapsed to smoking. Clinical characteristics differed between abstinent and relapsed smokers. Relapsed smokers were more likely than those abstinent from smoking to have a diagnosis of depression, posttraumatic stress disorder, or substance dependence disorder. Indeed, a substantial proportion of the relapsed smokers in our sample had a history of a mental health condition. For example, about one third of relapsed smokers had a history of depression. Relapse rates did not vary significantly among the participating VA facilities.

Nicotine replacement therapy was overwhelmingly the most commonly prescribed pharmacologic treatment during the index quit attempt. There were few prescriptions for combined pharmacologic treatment with NRT and bupropion SR. Fewer than 5% of respondents reported that they received the index prescription but did not use the medication.

Interest in Recycling and Treatment Preferences

Relapsed smokers were interested in recycling, and 65% wanted to make a repeat quit attempt within 30 days. Relapsed smokers were also interested in receiving repeat smoking cessation treatment. Among 233 who were interested in recycling within 30 days, 91% wanted pharmacologic treatment by itself or in combination with behavioral treatment, and 64% requested a combination of behavioral and pharmacologic treatment (

). Twenty-seven percent of smokers interested in recycling within 30 days wanted pharmacologic treatment only, 2% wanted behavioral treatment only, and 7% did not want any treatment. There was almost equal interest in the use of bupropion SR compared with NRT, and a significant proportion of smokers wanted to try NRT in combination with bupropion SR. To assess whether relapsed smokers who were interested in quitting within 30 days were interested in trying the same medication or a different medication from the one used during the index quit attempt in their next quit attempt, we compared smokers' pharmacologic treatment preference with their index prescription. For example, among participants who received any NRT during the index quit attempt, 41% were interested in using NRT again, 35% wanted to try bupropion SR, and 24% wanted to try NRT in combination with bupropion SR. Among participants who received bupropion SR during the index quit attempt, 42% wanted to use bupropion SR again, 38% wanted to try NRT, and 19% wanted to try NRT in combination with bupropion SR. These findings suggest that more than half of relapsed smokers want to try a different treatment in their next quit attempt.

The high level of interest in recycling within 30 days (n = 357) was consistent across various sociodemographic subgroups in bivariate analyses (

). There were no differences in age or mean time to the intervention telephone call between relapsed smokers who were interested in recycling within 30 days compared with those who were not. We observed no significant associations between mental health conditions and interest in recycling. In fact, individuals with a history of depression, posttraumatic stress disorder, or substance dependence disorder were just as interested in recycling within 30 days as individuals without such a history. However, black subjects were significantly more interested in recycling within 30 days compared with white subjects. Relapsed smokers who smoked more than 30 cigarettes per day were less interested in recycling within 30 days compared with relapsed smokers with lower smoking levels. There was also significant variation in interest in recycling across participating VA facilities.

Generalized linear mixed-model regression analyses controlling for VA facility were used to examine the independent predictors of interest in recycling within 30 days among 357 relapsed smokers (

). The following factors were not associated with interest in recycling and were eliminated from the regression model: age, sex, education, marital status, time to intervention telephone call, index prescription, diagnosis of diabetes mellitus, and presence of a mental health comorbid condition. Black race (odds ratio, 2.55; 95% confidence interval, 1.07-6.11) and having 2 or more smoking-related conditions (odds ratio, 1.79; 95% confidence interval, 0.99-3.24) were positively associated with interest in recycling, while being a heavy smoker (>30 cigarettes per day) (odds ratio, 0.27; 95% confidence interval, 0.12-0.61) was negatively associated with interest in recycling.

We also conducted bivariate analyses using the facility random-effects coefficient from the final regression model to examine the influence of institutional factors on interest in recycling. The facility random-effects coefficient is the odds ratio of the interest in recycling for a particular site compared with the "average" facility. The interest in recycling was higher in facilities with larger primary care patient volume (

= .001) and in facilities that had a formal mental health smoking cessation program (

= .047). Interest in recycling was lower in facilities that had a greater number of restrictions on the prescribing of tobacco dependence medications (

This study provides evidence that recently relapsed smokers are interested in trying to quit smoking again. In this analysis, approximately two thirds of relapsed smokers wanted to make a repeat cessation attempt within 30 days. The high level of interest in recycling was consistent across sociodemographic subgroups, and black subjects were especially interested in recycling. Independent predictors of interest in recycling included black race, lower smoking level, and greater number of smoking-related conditions. Most relapsed smokers were interested in pharmacologic treatment. In addition, there was significant interest in behavioral treatment combined with pharmacologic treatment.

These findings are encouraging because, despite failure, most relapsed smokers are ready to quit in the near future and want smoking cessation treatment. Furthermore, relapsed smokers who struggle with negative effect and depression are interested in recycling. Our results are similar to those of another study,

which found that relapsed smokers were interested in trying to quit again. In a cross-sectional survey of relapsed smokers, 98% expressed interest in quitting, and 50% were ready to quit immediately.

These findings do not necessarily imply that efforts to encourage repeat cessation attempts are cost-effective. The cost-effectiveness of repeat cessation treatment has not been specifically studied and would depend on the features of the intervention. It is probable that repeat cessation programs would be cost-effective, even if they are more intensive, because smoking cessation interventions in general are cost-effective.

Moreover, more costly and more intensive smoking cessation interventions yield greater benefit and a lower cost per quality-adjusted life-year saved than less intensive interventions.

Previous general cost-effective evaluations likely include substantial proportions of relapsed smokers because most smokers make multiple attempts to quit before achieving long-term abstinence.

Our results are consistent with those of prior studies,

which demonstrate that black smokers have high levels of motivation to quit and are more interested in quitting than white smokers. A possible explanation for this is that there is a higher proportion of light smokers among black subjects than among white subjects.

In this sample, although there were few differences between black subjects and white subjects, black subjects smoked significantly fewer cigarettes per day. However, this would only be a partial explanation because in the regression analysis black race predicted interest in recycling independent of the number of cigarettes smoked per day.

Our findings highlight missed opportunities to help smokers quit because most smoking cessation programs do not systematically contact participants who relapse. Clinicians may fail to instigate repeat treatment because of frustration with smokers' failure to quit.

Such frustration may result from the failure to recognize tobacco use as a chronic relapsing disorder and from the perception that tobacco dependence treatment has limited effectiveness. Furthermore, the acute care orientation of most healthcare practices is a significant barrier to treating chronic diseases and to addressing preventive health concerns.

Tobacco dependence has been compared with other chronic diseases such as diabetes, hypertension, and asthma; however, models for the treatment of tobacco dependence fail to incorporate principles of chronic disease treatment.

The strengths of this study are the significant proportion of black subjects in the sample and our successful identification of relapsed smokers using VA pharmacy databases. The study has several limitations. First, the sample was composed of smokers who received care from the VA, and the results may not be fully generalizable to smokers in the general population. Compared with the general population, veterans who obtain care from the VA are more likely to be older men of lower socioeconomic status, with a lower health-related quality of life due to a greater prevalence of comorbid chronic diseases and mental health conditions.

 Veterans are also more likely to smoke cigarettes and to be heavy smokers.

Nonetheless, the study findings are relevant for a large number of veterans who obtain care from the VA and who have dual Medicare or Medical Assistance Program eligibility. In addition, one might speculate that smokers in the general population would be more interested in recycling than smokers who are veterans.

Second, data on interest in recycling and treatment preferences were obtained by self-report and might be subject to social desirability bias. However, this is less likely when information is obtained over the telephone compared with in-person interview.

Third, the response rate for the intervention telephone call was 62%. It is possible that nonrespondent relapsed smokers might differ in their interest in recycling and their treatment preferences.

Fourth, there was variation among the facilities in smoking cessation prescribing policies and availability of behavioral counseling services. Although we controlled for facility in the multivariate analyses, it is possible that these site differences affected smokers' interest in recycling and their treatment preferences. In bivariate analyses, greater primary care patient volume and the presence of a mental health smoking cessation program were associated with greater interest in recycling, while increased restrictions on prescribing of tobacco dependence medications was associated with less interest in recycling. However, results of the bivariate analyses should be interpreted with caution because of the small number of sites.

Fifth, we were not able to examine in regression analyses whether interest in recycling was related to the abstinence duration of the index quit attempt. Abstinence duration would be an indicator of the participants' degree of success.

Sixth, we used information about demographics and clinical diagnoses from the VA's outpatient medical records, and there is limited information available about the accuracy (eg, sensitivity and specificity) of outpatient information. Two studies

examined the validity of the VA's outpatient medical records and found good reliability for demographic information and high sensitivity and specificity for diagnostic information.

Sean Nugent, BA

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