Objective: Most patients who misuse alcohol do not receive alcohol counseling from their providers. This study evaluated primary care patient and provider characteristics associated with receipt of alcohol-related advice and whether patients were advised to drink less or to abstain. Method: Outpatients from seven Veterans Affairs (VA) general medicine clinics were eligible if they screened positive for alcohol misuse, completed the Alcohol Use Disorders Identification Test (AUDIT) and answered questions about alcohol-related treatment and advice. Hierarchical logistic regression wag used to evaluate patient and provider characteristics associated with patient reports of alcohol-related advice from a primary care provider in the past year. Results: Among 5,191 patients with alcohol misuse in the past year, 1,554 (30%) reported receiving alcohol-related advice from their primary care provider during that time. Of patients advised, 73% reported advice to abstain. The likelihood of reporting advice increased as AUDIT scores increased: from 13% of patients with AUDIT scores <8 to 71% of those with scores greater than or equal to20. After adjustment for important confounders, measures reflecting the severity of alcohol misuse were most strongly associated with receipt of alcohol-related advice. Adjusted analyses also revealed increased odds of receiving advice among patients who reported liver disease, hypertension, current smoking or continuity of care. No measured provider characteristic was associated with giving advice in the fully adjusted model. Conclusions: This multisite VA study found that most patients with alcohol misuse did not receive alcohol counseling from a primary care provider. Moreover, providers predominantly offered advice to abstain, and they appeared to focus on patients with the most severe problems due to drinking or medical contraindications to drinking.

Angina and depression are common in ischemic heart disease (IHD), but their association remains understudied.

Patients with heart failure (HF) are at high risk of hospitalization or death. The objective of this study was to develop prediction models to identify patients with HF at highest risk for hospitalization or death. Using clinical and administrative databases, we identified 198,460 patients who received care from the Veterans Health Administration and had >= 1 primary or secondary diagnosis of HF that occurred within 1 year before June 1, 2009. We then tracked their outcomes of hospitalization and death during the subsequent 30 days and 1 year. Predictor variables chosen from 6 clinically relevant categories of sociodemographics, medical conditions, vital signs, use of health services, laboratory tests, and medications were used in multinomial regression models to predict outcomes of hospitalization and death. In patients who were in the >= 95th predicted risk percentile, observed event rates of hospitalization or death within 30 days and 1 year were 27% and 80% respectively, compared to population averages of 5% and 31%, respectively. The c-statistics for the 30-day outcomes were 0.82, 0.80, and 0.80 for hospitalization, death, and hospitalization or death, respectively, and 0.82, 0.76, and 0.77, respectively, for 1-year outcomes. In conclusion, prediction models using electronic health records can accurately identify patients who are at highest risk for hospitalization or death. This information can be used to assist care managers in selecting patients for interventions to decrease their risk of hospitalization or death. (C) 2012 Elsevier Inc. All rights reserved. (Am J Cardiol 2012;110:1342-1349)

IntroductionTo improve the health of overweight and obese veterans, the Department of Veterans Affairs (VA) developed the MOVE! Weight Management Program for Veterans. The aim of this evaluation was to assess its reach and effectiveness.MethodsWe extracted data on program involvement, demographics, medical conditions, and outcomes from VA administrative databases in 4 Western states. Eligibility criteria for MOVE! were being younger than 7 0 years and having a body mass index (BMI, in kg/m(2)) of at least 30.0, or 25.0 to 29.9 with an obesity-related condition. To evaluate reach, we estimated the percentage of eligible veterans who participated in the program and their representativeness. To evaluate effectiveness, we estimated changes in weight and BMI using multivariable linear regression.ResultsLess than 5% of eligible veterans participated, of whom half had only a single encounter. Likelihood of participation was greater in women, those with a higher BMI, and those with more primary care visits, sleep apnea, or a mental health condition. Likelihood of participation was lower among those who were younger than 5 5 (vs 55-64), widowed, current smokers, and residing farther from the medical center (>= 3 0 vs <30 miles). At 6- and 12-month follow-up, participants lost an average of 1.3 lb (95% confidence interval [CI], -2.6 to -0.02 lb) and 0.9 lb (95% CI, -2.0 to 0.1 lb) more than nonparticipants, after covariate adjustment. More intensive treatment (>= 6 encounters) was associated with greater weight loss at 12 months (-3.7 lb; 95% CI, -5.1 to -2.3 lb).ConclusionFew eligible patients participated in the program during the study period, and overall estimates of effectiveness were low.

Background:Statistical models that identify patients at elevated risk of death or hospitalization have focused on population subsets, such as those with a specific clinical condition or hospitalized patients. Most models have limitations for clinical use. Our objective was to develop models that identified high-risk primary care patients. Methods:Using the Primary Care Management Module in the Veterans Health Administration (VHA)’s Corporate Data Warehouse, we identified all patients who were enrolled and assigned to a VHA primary care provider on October 1, 2010. The outcome variable was the occurrence of hospitalization or death during the subsequent 90 days and 1 year. We extracted predictors from 6 categories: sociodemographics, medical conditions, vital signs, prior year use of health services, medications, and laboratory tests and then constructed multinomial logistic regression models to predict outcomes for over 4.6 million patients. Results:In the predicted 95th risk percentiles, observed 90-day event rates were 19.6%, 6.2%, and 22.6%, respectively, for hospitalization, death, and either hospitalization or death, compared with population averages of 2.7%, 0.7%, and 3.4%, respectively; 1-year event rates were 42.3%, 19.4%, and 51.3%, respectively, compared with population averages of 8.2%, 2.6%, and 10.8%, respectively. The C-statistics for 90-day outcomes were 0.83, 0.86, and 0.81, respectively, for hospitalization, death, and either hospitalization or death and were 0.81, 0.85, and 0.79, respectively, for 1-year outcomes. Conclusions:Prediction models using electronic clinical data accurately identified patients with elevated risk for hospitalization or death. This information can enhance the coordination of care for patients with complex clinical conditions.

Background: Patients with heart failure (HF) are at high risk of hospitalization and death. VHA has developed a population-based predictive model to identify high risk patients for case management. Methods: Using clinical and administrative databases, we identified all VA patients with a diagnosis of HF between June, 2008 and May, 2009, then followed the total of 194,062 HF patients for the subsequent 12 months. We used multinomial regression to model the outcome of hospitalization and death jointly. Candidates for predictor variables were related to demographics, medical history, vital status, health care utilization and medication. We randomly split the data 50% to 50% into a training sample and a validation sample. We derived the 30-day and 1-year predictive models from the training sample and validated the models on the other sample. Results: The C-statistics for 30-day and 1-year outcomes were 0.82 and 0.81 for hospitalization, 0.79 and 0.76 for hospitalization or death, respectively. Model calibration was excellent (Figure). For each outcome we stratified patients into 20 risk percentile categories. Risk stratification details were listed (Table). Conclusions: Predictive models can correctly stratify HF patients into risk categories for hospitalization and death. Table Risk Stratification by Outcomes 30-Day Outcomes 1-Year Outcomes Risk Hospitalized Hospitalized/Died Hospitalized Hospitalized/Died Category N (% * ) N (% * ) N (% * ) N (% * ) 1 5(0.05%) 36(0.4%) 51(0.5%) 535(5.5%) 2 9(0.1%) 72(0.7%) 121(1.2%) 824(8.5%) 3 21(0.2%) 99(1.0%) 223(2.3%) 1051(10.8%) 4 38(0.4%) 112(1.1%) 337(3.5%) 1254(12.9%) 5 51(0.5%) 117(1.2%) 472(4.9%) 1420(14.6%) 6 64(0.7%) 123(1.3%) 677(7.0%) 1628(16.9%) 7 93(1.0%) 185(1.9%) 907(9.3%) 1871(19.3%) 8 109(1.1%) 200(2.1%) 1105(11.4%) 2079(21.4%) 9 156(1.6%) 222(2.3%) 1397(14.4%) 2260(23.3%) 10 172(1.8%) 255(2.6%) 1664(17.1%) 2538(26.2%) 11 231(2.4%) 305(3.1%) 1980(20.4%) 2818(29.0%) 12 273(2.8%) 365(3.8%) 2306(23.8%) 3020(31.1%) 13 333(3.4%) 462(4.8%) 2589(26.7%) 3502(36.1%) 14 419(4.3%) 451(4.6%) 3057(31.5%) 3766(38.8%) 15 480(4.9%) 591(6.1%) 3423(35.3%) 4206(43.3%) 16 639(6.6%) 717(7.4%) 3989(41.1%) 4691(48.4%) 17 764(7.9%) 891(9.2%) 4446(45.8%) 5208(53.7%) 18 977(10.1%) 1126(11.6%) 5004(51.6%) 5819(60.0%) 19 1296(13.4%) 1458(15.0%) 5775(59.5%) 6619(68.2%) 20 2208(22.7%) 2580(26.6%) 6898(71.1%) 7812(80.5%) All 8338(4.3%) 10367(5.3%) 46421(23.9%) 62921(32.4%) *Observed event rate per risk category of the corresponding outcome

Background: Little is known about geographic differences in health status among patients with chronic obstructive pulmonary disease (COPD). Objectives: The aim of this study was to examine regional variations in self-reported health status of COPD patients at 7 Veterans Affairs clinics. Methods: The Ambulatory Care Quality Improvement Project was a multicenter, randomized trial conducted from 1997 to 2000 that evaluated a quality improvement intervention in the primary care setting. Four thousand and nine participants with COPD (age ≧45 years) completed the Seattle Obstructive Lung Disease Questionnaire (SOLDQ) and 2,991 also completed the Medical Outcomes Study 36-item short form (SF-36). The unadjusted maximal difference in health status scores is reported as the ratio of the highest and lowest site prevalence. We report the maximal site difference in mean health status scores after adjusting for demographics, comorbidities, utilization, medication use and clinic factors. Results: Subjects were predominantly older (66.5 ± 9.2 years) Caucasian (83.2%) men (97.9%). After adjustment, the maximal site difference for each health status score was significant (p < 0.01) but larger for the SOLDQ (physical 11.2, emotional 9.7, coping skills 7.6) than for the SF-36 (physical component summary 4.7, mental component summary 2.6). Most of the health status variation was explained by individual or clinic level factors, not clinic site. Conclusions: Our models explained <30% of variation in health status measures; therefore, future studies should consider additional predictors of health status such as physical performance, social determinants of health, COPD treatment and environmental factors. Despite its limitations, this study suggests a need to consider regional differences in health status when comparing COPD health outcomes in diverse geographic areas.

BACKGROUND CART is a clinical software application that is integrated in the VA's electronic health record (EHR) and supports a national data repository and quality improvement program. From 2004-2009, CART was developed and implemented in the 77 VA cath labs. The goal of this project was to measure variations in implementation of CART across VA cath labs, and identify facilitators and barriers to successful implementation. METHODS The rate and degree of implementation of CART, from first contact with a site through technical installation and then clinical adoption, was assessed via the CART tracking database and by comparing procedure entries in CART with site case logs at three time points. Clinical adoption by site was described at each time as full (>90% concordance), partial (1-90%), or none (0%). To assess facilitators and barriers, we surveyed clinical champions at each of the sites using a structured, web-based survey. RESULTS Between June, 2004-May, 2007, 59 sites (77%) had either fully or partially adopted CART; by April, 2009, 74 sites (96%) had fully (71%; 52) or partially adopted (29%; 21) CART. Technical installation took a median of 3 ± 4.2 months, while the time to clinical adoption, defined as the time from installation completion to active use, took a median of 1 ± 7.6 months. Survey responses were obtained from 64 sites (83% of sites). Sites felt integration of CART within the VA EHR was extremely important (74%; 47). The majority agreed that CART would improve current processes (80%; 51). The majority also believed CART was strategically important to VA quality improvement (76%; 49). Among the top facilitators noted were CART's integration within the VA EHR, future research potential using CART, and the desire to improve quality. Respondents cited contentment with current processes, concern regarding staff resources, and lack of interfacing with proprietary hemodynamic systems as top barriers. CONCLUSIONS CART represents a successful nationwide health IT implementation. Throughout the process, however, success was dependent on a balance of clinical and technical priorities. The variation in implementation and the strategies learned through this process may be applicable to other health IT implementation projects.

BACKGROUND:Accumulating evidence suggests that collaborative models of care enhance communication among primary care providers, improving quality of care and outcomes for patients with chronic conditions. We sought to determine whether a multifaceted intervention that used a collaborative care model and was directed through primary care providers would improve symptoms of angina, self-perceived health, and concordance with practice guidelines for managing chronic stable angina.METHODS:We conducted a prospective trial, cluster randomized by provider, involving patients with symptomatic ischemic heart disease recruited from primary care clinics at 4 academically affiliated Department of Veterans Affairs health care systems. Primary end points were changes over 12 months in symptoms on the Seattle Angina Questionnaire, self-perceived health, and concordance with practice guidelines.RESULTS:In total, 183 primary care providers and 703 patients participated in the study. Providers accepted and implemented 91.6% of 701 recommendations made by collaborative care teams. Almost half were related to medications, including adjustments to β-blockers, long-acting nitrates, and statins. The intervention did not significantly improve symptoms of angina or self-perceived health, although end points favored collaborative care for 10 of 13 prespecified measures. While concordance with practice guidelines improved 4.5% more among patients receiving collaborative care than among those receiving usual care (P < .01), this was mainly because of increased use of diagnostic testing rather than increased use of recommended medications.CONCLUSION:A collaborative care intervention was well accepted by primary care providers and modestly improved receipt of guideline-concordant care but not symptoms or self-perceived health in patients with stable angina.

The VA Cardiovascular Assessment, Reporting, and Tracking (CART) system is a customized electronic medical record system which provides standardized report generation for cardiac catheterization procedures, serves as a national data repository, and is the centerpiece of a national quality improvement program. Like many health information technology projects, CART implementation did not proceed without some barriers and resistance. We describe the nationwide implementation of CART at the 77 VA hospitals which perform cardiac catheterizations in three phases: (1) strategic collaborations; (2) installation; and (3) adoption. Throughout implementation, success required a careful balance of technical, clinical, and organizational factors. We offer strategies developed through CART implementation which are broadly applicable to technology projects aimed at improving the quality, reliability, and efficiency of health care.

Background: Mortality from acute myocardial infarction (AMI) is declining worldwide. We sought to determine if mortality in the Veterans Health Administration (VHA) has also been declining.Methods: We calculated 30-day mortality rates between 2004 and 2006 using data from the VHA External Peer Review Program (EPRP), which entails detailed abstraction of records of all patients with AMI. To compare trends within VHA with other systems of care, we estimated relative mortality rates between 2000 and 2005 for all males 65 years and older with a primary diagnosis of AMI using administrative data from the VHA Patient Treatment File and the Medicare Provider Analysis and Review (MedPAR) files.Results: Using EPRP data on 11,609 patients, we observed a statistically significant decline in adjusted 30-day mortality following AMI in VHA from 16.3% in 2004 to 13.9% in 2006, a relative decrease of 15% and a decrease in the odds of dying of 10% per year (p = .011). Similar declines were found for in-hospital and 90-day mortality.Based on administrative data on 27,494 VHA patients age 65 years and older and 789,400 Medicare patients, 30-day mortality following AMI declined from 16.0% during 2000-2001 to 15.7% during 2004-June 2005 in VHA and from 16.7% to 15.5% in private sector hospitals. After adjusting for patient characteristics and hospital effects, the overall relative odds of death were similar for VHA and Medicare (odds ratio 1.02, 95% C.I. 0.96-1.08).Conclusion: Mortality following AMI within VHA has declined significantly since 2003 at a rate that parallels that in Medicare-funded hospitals.

BACKGROUND:In the United States, relatively little is known about cause of death in individuals who die prior to or after hospital discharge for acute coronary syndromes (ACS). The purpose of this report was to compare baseline patient characteristics according to whether the underlying cause of death was cardiac or non-cardiac.METHODS:We linked cause of death information from Washington State death records to the Department of Veterans Affairs (VA) External Peer Review Program ACS registry. From 524 individuals who were hospitalized for ACS in veterans hospitals located in Washington State or Oregon, we identified 136 individuals who according to VA death records died during the years 2003 to 2005. Of these, 117 (86%) were found in Washington State death records. Sociodemographic variables, as well as underlying and secondary causes of death, were obtained from Washington State death records provided by the Washington State Department of Health. Clinical variables, including medical histories, presentation on admission, and in-hospital death were extracted from the VA ACS registry.RESULTS:Somewhat surprisingly, only 52% of veterans died of cardiac causes when only the underlying cause of death was used. However, when secondary causes of death were added to the definition, the proportion that died of cardiac causes increased to 81%. Patient characteristics were similar in the two groups, although small numbers limited the ability to detect statistically significant differences.CONCLUSION:These preliminary findings suggest that it is important to consider secondary causes as well as the underlying one when classifying deaths as cardiac or non-cardiac.

Objective: To determine whether a history of depression and/or posttraumatic stress disorder (PTSD) is associated with all-cause mortality in primary care patients over an average of 2 years. Methods: Patients from seven Department of Veterans Affairs medical centers completed mailed questionnaires. Depression and PTSD status were determined from patient self-report of a prior diagnosis and/or electronic administrative data. Date of death was ascertained from Veterans Health Information Systems and Technology Architecture and the Department of Veterans Affairs' Beneficiary Identification and Records Locator System. Results: Among 35,715 primary care patients, those with a history of depression without a history of PTSD (n = 6876) were at increased risk of death over an average of 2 years compared with patients with neither depression nor PTSD after adjustment for demographic variables, health behaviors, and medical comorbidity (hazard ratio (HR) = 1.17; 95% Confidence Interval (CI) = 1.06–1.28). However, patients with a history of PTSD without a history of depression (n = 748) were not at increased risk of death compared with patients with neither depression nor PTSD (HR = 0.84; 95% CI = 0.63–1.13). Patients with a history of both (n = 3762) were at increased risk of death after adjustment for demographic factors, although not after additional adjustment for health behaviors and medical comorbidity (HR = 0.90; 95% CI = 0.78–1.04). Conclusions: In a large sample of veterans, a prior diagnosis of depression, but not PTSD, was associated with an increased risk of death over an average of 2 years after adjusting for age, demographic variables, health behaviors, and medical comorbidity. PTSD = posttraumatic stress disorder; VA= Department of Veterans Affairs; ACQUIP = Ambulatory Care Quality Improvement Project; MHI-5 = Mental Health Inventory; CHF = congestive heart failure; MI = heart attack/myocardial infarction.

Rationale and Objectives: Lung cancer is a frequent cause of death among patients with chronic obstructive pulmonary disease (COPD). We examined whether the use of inhaled corticosteroids among patients with COPD was associated with a decreased risk of lung cancer.Methods: We performed a cohort study of United States veterans enrolled in primary care clinics between December 1996 and May 2001. Participants had received treatment for, had an International Classification of Disease, 9th edition, diagnosis of, or a self-reported diagnosis of COPD. Patients with a history of lung cancer were excluded. To be exposed, patients must have been at least 80% adherent to inhaled corticosteroids. We used Cox regression models to estimate the risk of cancer and adjust for potential confounding factors.Findings: We identified 10,474 patients with a median follow-up of 3.8 years. In comparison to nonusers of inhaled corticosteroids, adjusting for age, smoking status, smoking intensity, previous history of non-lung cancer malignancy, coexisting illnesses, and bronchodilator use, there was a dose-dependent decreased risk of lung cancer associated with inhaled corticosteroids (ICS dose < 1,200 mu g/d: adjusted HR, 1.3; 95% confidence interval, 0.67-1.90; ICS dose >= 1,200 mu g/d: adjusted HR, 0.39; 95% confidence interval, 0.16-0.96). Changes in cohort definitions had minimal effects on the estimated risk. Analyses examining confounding by indication suggest biases in the opposite direction of the described effects.Interpretation: Results suggest that inhaled corticosteroids may have a potential role in lung cancer prevention among patients with COPD. These initial findings require confirmation in separate and larger cohorts.

Background: There are many measures of refill adherence available, but few have been designed or validated for use with repeated measures designs and short observation periods. Objective: To design a refill-based adherence algorithm suitable for short observation periods, and compare it to 2 reference measures. Methods: A single composite algorithm incorporating information on both medication gaps and oversupply was created. Electronic Veterans Affairs pharmacy data, clinical data, and laboratory data from routine clinical care were used to compare the new measure, ReComp, with standard reference measures of medication gaps (MEDOUT) and adherence or oversupply (MEDSUM) in 3 different repeated measures medication adherence-response analyses. These analyses examined the change in low density lipoprotein (LDL) with simvastatin use, blood pressure with antihypertensive use, and heart rate with β-blocker use for 30- and 90-day intervals. Measures were compared by regression based correlations (R2 values) and graphical comparisons of average medication adherence-response curves. Results: In each analysis, ReComp yielded a significantly higher R2 value and more expected adherence-response curve regardless of the length of the observation interval. For the 30-day intervals, the highest correlations were observed in the LDL-simvastatin analysis (ReComp R2 = 0.231; [95% CI, 0.222–0.239]; MEDSUM R2 = 0.054; [95% CI, 0.049–0.059]; MEDOUT R2 = 0.053; [95% CI, 0.048–0.058]). Conclusions: ReComp is better suited to shorter observation intervals with repeated measures than previously used measures.

The Journal of Alternative and Complementary MedicineVol. 13, No. 2 Letters to The EditorComplementary And Alternative Medicine Use among Veterans Affairs OutpatientsMark A. Micek, Katharine A. Bradley, Clarence H. BraddockIII, Charles Maynard, Mary McDonell, and Stephan D. FihnMark A. MicekSearch for more papers by this author, Katharine A. BradleySearch for more papers by this author, Clarence H. BraddockIIISearch for more papers by this author, Charles MaynardSearch for more papers by this author, Mary McDonellSearch for more papers by this author, and Stephan D. FihnSearch for more papers by this authorPublished Online:27 Mar 2007https://doi.org/10.1089/acm.2006.6147AboutSectionsPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail FiguresReferencesRelatedDetailsCited byNonpharmacological Treatment of Army Service Members with Chronic Pain Is Associated with Fewer Adverse Outcomes After Transition to the Veterans Health Administration28 October 2019 | Journal of General Internal Medicine, Vol. 35, No. 3Biopsychosocial benefits of movement-based complementary and integrative health therapies for patients with chronic conditions18 June 2018 | Chronic Illness, Vol. 16, No. 1The Association of Complementary Therapy Use With Prescription Medication Adherence Among Older Community-Dwelling Adults28 August 2015 | Journal of Applied Gerontology, Vol. 36, No. 9Controlled Rhythmic Yogic Breathing as Complementary Treatment for Post-Traumatic Stress Disorder in Military Veterans: A Case Series Joseph Walker and Deborah Pacik1 August 2017 | Medical Acupuncture, Vol. 29, No. 4Complementary and alternative medicine (CAM) following traumatic brain injury (TBI): Opportunities and challengesBrain Research, Vol. 1640Integration of Chiropractic Services in Military and Veteran Health Care Facilities16 December 2015 | Journal of Evidence-Based Complementary & Alternative Medicine, Vol. 21, No. 2Yoga as an Intervention for PTSD: a Theoretical Rationale and Review of the Literature30 January 2016 | Current Treatment Options in Psychiatry, Vol. 3, No. 1Mindfulness-based Stress Reduction in Addition to Usual Care Is Associated with Improvements in Pain, Fatigue, and Cognitive Failures Among Veterans with Gulf War IllnessThe American Journal of Medicine, Vol. 129, No. 2The use of complementary and alternative medicine in adults with depressive disorders. A critical integrative reviewJournal of Affective Disorders, Vol. 179The integrative management of PTSD: A review of conventional and CAM approaches used to prevent and treat PTSD with emphasis on military personnelAdvances in Integrative Medicine, Vol. 2, No. 1Incorporating Complementary and Alternative Practices into Treatment of PTSD23 June 2015A Factor Analysis and Exploration of Attitudes and Beliefs Toward Complementary and Conventional Medicine in VeteransMedical Care, Vol. 52, No. Supplement 5Loving-Kindness Meditation and the Broaden-and-Build Theory of Positive Emotions Among Veterans With Posttraumatic Stress DisorderMedical Care, Vol. 52, No. Supplement 5CAM Utilization Among OEF/OIF VeteransMedical Care, Vol. 52, No. Supplement 5Loving-Kindness Meditation for Posttraumatic Stress Disorder: A Pilot Study25 July 2013 | Journal of Traumatic Stress, Vol. 26, No. 4 Volume 13Issue 2Mar 2007 InformationMary Ann Liebert, Inc.To cite this article:Mark A. Micek, Katharine A. Bradley, Clarence H. BraddockIII, Charles Maynard, Mary McDonell, and Stephan D. Fihn.Complementary And Alternative Medicine Use among Veterans Affairs Outpatients.The Journal of Alternative and Complementary Medicine.Mar 2007.190-193.http://doi.org/10.1089/acm.2006.6147Published in Volume: 13 Issue 2: March 27, 2007PDF download

BACKGROUND We sought to examine health care resource utilization in the last 6 months of life among patients who died with chronic obstructive pulmonary disease (COPD) compared with those who died with lung cancer and to examine geographic variations in care. METHODS We performed a retrospective cohort study of patients diagnosed as having COPD or lung cancer, who were seen in 1 of 7 Veteran Affairs medical centers primary care clinics and who died during the study period. Our outcome of interest was health care resource utilization in the last 6 months of life. RESULTS In the last 6 months of life, patients with COPD were more likely to visit their primary care providers but had fewer hospital admissions compared with patients with lung cancer. Patients with COPD had twice the odds of being admitted to an intensive care unit (ICU), 5 times the odds of remaining there 2 weeks or longer, and received fewer opiates and benzodiazepine prescriptions compared with patients with lung cancer. There were geographic variations in the use of ICUs for patients with COPD but not for those with lung cancer. Total health care costs were $4000 higher for patients with COPD because of ICU utilization. CONCLUSIONS In the last 6 months of life, patients with COPD were more likely to have had a primary care visit and been admitted to an ICU but less likely to receive palliative medications compared with patients with lung cancer. We found significant geographic variability in ICU utilization but only for patients with COPD.