Asian and Pacific Islanders (APIs) constitute less than 6% of the US population, but account for more than half of Americans with chronic hepatitis B virus (HBV) infection. We sought to examine the effect of country of origin on HBV testing and chronic HBV infection prevalence among APIs.

We analyzed demographic and clinical data collected for adults from Kaiser Permanente Hawaii with 1 or more healthcare encounters during 2006 to 2008, 12 months or more of follow-up before 2009, and no HBV-related diagnosis within 6 months of enrollment. Persons who received a test and a positive test result for HBV surface antigen or HBV DNA were classified “tested” and with “chronic HBV infection,” respectively.

 Of 92,687 eligible APIs, 53,573 (58%) had country-of-origin data available. Among those, 41,263 were US born; 28.3% were tested; and 1.8% of those tested had chronic HBV infection. Of 12,310 foreign-born APIs, 30.5% were tested and 7.4% of those tested had chronic HBV infection. Foreignborn APIs had higher odds of being tested (odds ratio [OR] = 1.15) and testing positive (OR = 4.18) compared with US-born APIs. Persons with 2 or more abnormal tests for alanine aminotransferase (ALT) levels had higher odds of getting tested (OR = 6.12) and of testing positive (OR = 1.86) compared with persons with other ALT levels.

 Less than one-third of this managed care API population (29% of 53,573) was tested, yet the prevalence of chronic HBV infection (3.2%) was 12 times higher than that of the general US population. These findings underscore the importance of adherence to HBV testing guidelines to identify persons with infection so they may be linked to care.

In the United States, between 800,000 and 2 million people live with chronic hepatitis B virus (HBV) infection and 2000 to 4000 deaths are attributed to HBV infection annually.

 Cirrhosis and hepatocellular carcinoma (HCC) are 2 major long-term complications of chronic HBV infection that significantly increase morbidity and mortality. Asian and Pacific Islanders (APIs) make up less than 6% of the US population but account for more than half of Americans living with chronic HBV infection

; hepatocellular carcinoma (HCC) rates are highest among APIs, and HCC is a leading cause of cancer deaths in this population.

 Most APIs with chronic HBV infection acquire their infection at birth through mother-to-child transmission or during childhood.

In 2009, an estimated 1.32 million foreign-born individuals with chronic HBV infection were thought to be living in the United States, and 58% of those persons migrated from Asia, where HBV infection is highly endemic.

 The population makeup of Hawaii, the 50th US state, includes 47% persons of Asian descent and 29% persons of Native Hawaiian/Pacific Islander descent.

Among Hawaii’s 212,229 foreign-born residents in 2000, 73% were born in countries with endemic HBV infection, including China, Vietnam, the Philippines, and elsewhere among the South Pacific Islands.

In Hawaii, Kaiser Permanente (KPHI) has approximately 220,000 members and is the largest health maintenance organization (HMO) providing care in the state.

Commensurate with the high representation of APIs living in Hawaii, APIs constitute approximately half of KPHI’s membership. Given the unique demographic conditions in Hawaii relative to other US states, and our access to demographic and clinical data collected from patient encounters, we sought to examine the effect of country of origin on HBV infection testing and prevalence among APIs at KPHI.

Persons eligible for the study included those who: (1) were aged 18 years or older; (2) had at least 1 KPHI clinical encounter at any time during January 1, 2006, to December 31, 2008; and (3) completed at least 12 months of continuous follow-up any time before 2009. To examine HBV infection testing and prevalence among persons without previously diagnosed infection, we excluded those with any HBV-related 

International Classification of Diseases, Ninth Revision

) diagnosis code within 6 months of their first KPHI clinical encounter. Retrospective electronic data were available through January 1, 1998.

Patient data were collected from electronic medical records and included age (as of last encounter before December 31, 2008), gender, race/ethnicity, annual income (derived from census tract data based on geocode), and serum alanine aminotransferase (ALT) level. Abnormal ALT was defined as a level higher than the lab-specific upper limit of normal value (range: 3-63 IU/L). Data were collected from the date of the patient’s earliest health plan enrollment through the last health plan encounter, ending December 31, 2008. Methods were identical to those used in an earlier analysis of hepatitis testing and prevalence among 1.2 million persons in 4 healthcare organizations including KPHI, but which lacked country-of-origin data.

 Briefly, persons were classified as “tested” if they had at least 1 test performed for HBV surface antigen (HBsAg) or a qualitative or quantitative test for HBV DNA; those classified with “HBV infection” had at least 1 positive result for any of these tests. The number of persons with infection divided by the number of persons tested was calculated to determine HBV infection prevalence. APIs were classified as foreign born (birth in Asian or Pacific Island countries) or US born (birth in the United States or Canada), based on the self-reported country of origin.

This study was reviewed and approved by the KPHI Institutional Review Board and followed guidelines of the US Department of Health and Human Services for protection of human subjects.

Statistical analysis was performed using SAS, version 9.2 (SAS Institute Inc, Cary, North Carolina). Chi-squared test (χ²) was used to ascertain differences in characteristics between US-born and foreign-born APIs.

After exclusion of 198 persons with an HBV-related ICD-9 diagnosis code within 6 months of their first KPHI clinical encounter, a total of 189,550 KPHI adults met the study inclusion criteria. Among eligible adults, 92,687 (48.9%) were APIs. Information on country of origin was available for 53,573 persons: 12,310 (23%) were born in Asian and Pacific Island countries and 41,263 (77%) were born in the United States (n = 41,235) or Canada (n = 28). Among all APIs (N = 53,575), 29% (n = 15,439) were tested for HBV infection and, among those tested, 3.2% (n = 494) were infected.

The principal demographic and clinical characteristics of the US and foreign-born APIs are presented in 

. Compared with foreign-born APIs, US-born APIs were more likely to be younger than age 30 years or older than age 69 years. Foreign-born APIs were more likely to be female, to have lower annual incomes, and to have had at least 2 abnormal ALT test results before undergoing HBV infection testing. A greater proportion of foreign-born APIs were tested and a greater proportion had HBV infection compared with US-born APIs (tested: 3752 [30.5%] vs 11,687 [28.3%]; tested positive: 279 [7.4%] vs 215 [1.8%]). Among APIs with at least 2 abnormal ALT test results, 46% of foreign-born and 41% of US-born were tested for HBV infection, 8.6% of foreign born and 1.9% of US-born were infected.

show the important factors associated with odds of getting tested and testing positive for HBV infection. Individuals aged 30 to 39 years had higher odds of getting tested and of testing positive than other age groups. Females had higher odds of getting tested than males. Repeating the analysis excluding the pregnant women who were ever tested for HBsAg (n = 7092), the odds ratio (OR) for females attenuated from 3.26 to 1.13 (data not shown). Foreign-born APIs had higher odds of being tested (OR = 1.15) and testing positive (OR = 4.18) compared with USborn APIs. Persons with at least 2 or more abnormal ALT tests had higher odds of getting tested (OR = 6.12) and testing positive (OR = 1.86) compared with other ALT levels. Repeating the analysis with pregnant women excluding OR for foreign-born APIs and ALT levels did not produce results that differed much.

 shows the proportion of foreign-born APIs who were tested and infected with HBV by country of birth. Among the 12,310 foreign-born APIs, 11,058 (89.8%) were from countries with high HBV endemicity (ie, HBsAg prevalence ≥8%)16; of these, 29.9% were tested for HBV infection and 7.9% of those were positive for HBV infection. The remaining 10.2% of foreign-born APIs were from countries with intermediate HBV endemicity (ie, HBsAg prevalence 2%-7%)

; of these, 29.8% were tested and 2.7% were positive for HBV infection. Among all foreign-born APIs tested, the proportion of those infected ranged from 2.7% (Japan) to 12.3% (China). Approximately 50% of the foreign-born APIs were from the Philippines; of these, 29.9% were tested and 5.5% were positive for HBV infection.

In this study of HBV testing and infection prevalence among APIs in a large HMO, we found that less than onethird of persons without a HBV diagnosis code at enrollment were tested for HBV infection. Among APIs who were tested, the prevalence of HBV infection (3.2%) was almost 12 times higher than that of the general US population (HBsAg prevalence 0.27%). Among foreign-born APIs who were tested, 90% of whom were born in countries with high HBV endemicity, infection prevalence was largely commensurate with that of their country of origin (7.5% overall). These findings support US Centers for Disease Control and Prevention (CDC) guidelines that recommend HBV infection screening for pregnant women, persons with high-risk behavior, and those born in Asia, Africa, and other geographic areas where HBsAg prevalence exceeds 2%.

Even among US-born APIs, the prevalence of HBV infection was high (1.8%) relative to the general US population. Maternal country of birth information among USborn APIs might have permitted differentiation of first generation “API Americans” from those whose families had resided in the United States for multiple generations, and may have permitted further differentiation of infection prevalence; however, we did not have access to such information.

Results were compared with those from other studies. For example, a study by Lin et al reported HBV prevalence to be almost double (6.2%, 185 of 2973) that found in our study among previously undiagnosed API adults, and foreign-born APIs (10.7% HBsAg-positive test result) were approximately 20 times more likely to be chronically infected than US-born APIs (0.7% HBsAg-positive test result; RR 19.4, 95% CI, 2.6-141.8.

 Two other US-based studies—1 conducted in Hawaii (overall HBsAg prevalence, 3.6%)

 and another that used university students in California as study subjects (overall HBsAg prevalence of 2.3%, 1.4% in US-born and 3.3% in foreign-born APIs)

— assessed HBV infection prevalence, and results of those studies more closely aligned with our results, although these studies did not limit their analysis to APIs and had an age range of 18 to 21 years. In our study, perhaps most remarkable was that among APIs with a history of at least 2 abnormal ALT tests, fewer than 50% were tested for HBV infection. CDC and the American Association for the Study of Liver Diseases guidelines recommend testing persons with unexplained abnormal liver enzyme tests for HBV (and hepatitis C virus) infection.

Our study had a number of limitations. In order to ensure assessment of persons with sustained followup, eligibility criteria for the study required at least 12 months of continuous follow-up any time before 2009. We acknowledge that some persons could have been tested in 2009 or thereafter and not included in our analysis. In addition, although we excluded those with an HBV 

 diagnosis code within 6 months of their first clinical encounter, some persons could have previously tested negative for HBV infection or been vaccinated for HBV in another healthcare setting prior to our period of study, and might not need to be tested again. Secondly, from a case definition standpoint, we used a single HBsAg or HBV DNA to define testing for and infection with HBV. Although most of these infections were chronic, it is conceivable that some were actually acute. Given the low incidence of acute HBV infection in the United States, however, it is unlikely that such misclassification would have appreciably affected our results.

Finally, we recognize that the estimates of testing frequency and infection prevalence in our cohort are not necessarily reflective of all private and public healthcare systems in the United States. Nonetheless, our study had a large sample size and was conducted in a setting with high representation of persons of API descent.

In summary, we found that less than one-third of this API population in managed care were tested for HBV infection and that among those tested, the prevalence of HBV infection was nearly 12 times higher than that of the overall US population. Infection prevalence among foreign-born APIs was similar to that of their country of origin. The prevalence of HBV infection among US-born APIs was almost 7 times higher than for the overall US population. These findings underscore the importance of adherence to current US guidelines that recommend HBV testing of persons born in countries with intermediate and high HBV endemicity, and the testing of US-born persons whose parents were born in such countries. At KPHI, country-of-birth information is not collected routinely. The results of this paper might provide evidence of the importance of collecting such information routinely in our health system. Sensitivity analysis has shown the costeffectiveness of HBV screening, even with the prevalence of chronic HBV infection as low as 1%,

and hence it is important to focus HBV screening efforts on high-prevalence groups such as foreign-born APIs. Persons found to be susceptible to infection should be vaccinated, and those found to have infection should be evaluated for treatment.

Kaiser Permanente Center for Health Research Hawai’i, Honolulu, HI (VV, CN); Centers for Disease Control and Prevention (CDC), National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention, Atlanta, GA (PRS, ACM, EHT); Henry Ford Health System, Detroit, MI (ML, SCG); Kaiser Permanente Center for Health Research Northwest, Portland, OR (EH); Geisinger Health System, Danville, PA (JAB).

CHeCS was funded by the CDC Foundation, which received grants from Abbott Laboratories; Genentech, a member of the Roche Group; Janssen Pharmaceutical Companies of Johnson & Johnson; and Vertex Pharmaceuticals.

 The authors (VV, PRS, ACM, LBR, ML, SCG, EH, JAB, EHT, CN) report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article. The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the CDC.

Concept and design (VV, ACM, ML, SCG, EH, EHT, CN); acquisition of data (VV, ACM, LBR, ML, SCG, EH, EHT, CN); analysis and interpretation of data (VV, PRS, ACM, EH, JAB, EHT, CN); drafting of the manuscript (VV, PRS, ACM, JAB, EHT); critical revision of the manuscript for important intellectual content (VV, PRS, ACM, LBR, SCG, EH, JAB, EHT, CN); statistical analysis (VV, ML); provision of study materials or patients (LBR, SCG, EH); administrative, technical, or logistic support (ACM); supervision (PRS, ACM).

 Vinutha Vijayadeva, PhD, MPH, Kaiser Permanente Center for Health Research Hawaii, 501 Alakawa St, Ste 201, Honolulu, HI 96817. E-mail: vinutha.x.vijayadeva@kp.org.1. Wasley A, Kruszon-Moran D, Kuhnert W, et al. The prevalence of hepatitis B infection in the United States in the era of vaccination. J Infect Dis. 2010;202(2):192-201.

2. Kowdley KV, Wang CC, Welch S, Roberts H, Brosgart CL. Prevalence of chronic hepatitis B among foreign-born persons living in the United States by country of origin. Hepatology. 2012;56(2):422-433.

3. Weinbaum CM, Williams I, Mast EE, et al. Recommendations for identification and public health management of persons with chronic hepatitis B virus infection. MMWR Recomm Rep. 2008;57(RR-8):1-20.

4. Vogt T, Wise ME, Shih H, Williams IT. Hepatitis B mortality in the United States, 1990-2004 [abstract]. 45th Annual Meeting of Infectious Diseases Society of America, San Diego, CA; October 4-7, 2007.

5. 2010 Census Briefs. Overview of Race and Hispanic Origin: 2010. US Census Bureau website. http://www.census.gov/prod/cen2010/briefs/ c2010br-02.pdf. Accessed November 29, 2012.

6. Colvin HM, Mitchell AE, eds. Hepatitis and Liver Cancer: A National Strategy for Prevention and Control of Hepatitis B and C. Institute of Medicine; Committee on the Prevention and Control of Viral Hepatitis Infection. Washington, DC: National Academies Press; 2009.

7. Centers for Disease Control and Prevention. Hepatocellular carcinoma — United States, 2001-2006. MMWR Morb Mortal Wkly Rep. 2010;59(17):517-520.

8. Altekruse SF, McGlynn KA, Reichman ME. Hepatocellular carcinoma incidence, mortality, and survival trends in the United States from 1975 to 2005. J Clin Oncol. 2009;27(9):1485-1491.

9. Chang ET, Yang J, Alfaro-Velcamp T, So SK, Glaser SL, Gomez SL. Disparities in liver cancer incidence by nativity, acculturation, and socioeconomic status in California Hispanics and Asians. Cancer Epidemiol Biomarkers Prev. 2010;19(12):3106-3118.

10. Chronic hepatitis B in Asian Americans, Native Hawaiians and other Pacific Islanders: background. US Department of Health and Human Services, Office of Minority Health website. http://minorityhealth .hhs.gov/templates/content.aspx?ID=7240. Updated December 17, 2008. Accessed April 3, 2014.

11. 2010 Census Briefs. The Asian population: 2010. US Census Bureau website. http://www.census.gov/prod/cen2010/briefs/c2010br-11.pdf. Accessed November 20, 2012.

12. 2010 Census Briefs. The Native Hawaiian and other Pacific Islander Population: 2010. US Census Bureau website. http://www.census.gov/ prod/cen2010/briefs/c2010br-12.pdf. Accessed November 20, 2012.

13. Tsai NCS, Holck PS, Wong LL, Ricalde AA. Seroepidemiology of hepatitis B virus infection: analysis of mass screening in Hawaii. Hepatol Int. 2008;2(4):478-485.

14. Kaiser Foundation Health Plan - Hawaii Region November 2012. http://web2/financeweb-admin/UltimateEditorInclude/ UserFiles/Reports/Membership%20Reports/2012/11Nov/Nov2012_ MembershipRpt_w_comments.pdf. Accessed: November 20, 2012.

15. Spradling PR, Rupp L, Moorman AC, et al. Hepatitis B and C virus infection among 1.2 million persons with access to care: factors associated with testing and infection prevalence. Clin Infect Dis. 2012;55(8):1047-1055.

16. Averhoff F; Centers for Disease Control and Prevention. Hepatitis B. http://wwwn.cdc.gov/travel/yellowbookch4-HepB.aspx. Accessed November 20, 2012.

17. Lin SY, Chang ET, So SK. Why we should routinely screen Asian American adults for hepatitis B: a cross-sectional study of Asians in California. Hepatology. 2007;46(4):1034-1040.

18. Quang Y, Vu J, Yuk J, Li C-S, Chen M, Bowlus C. Prevalence of hepatitis B surface antigen in US-born and foreign-born Asian/Pacific Islander college students. J Am Coll Health. 2010;59(1):37-41.

19. American Association for the Study of the Liver Disease. Viral hepatitis prevention, screening, and treatment. http://www.aasld.org/ patients/pages/viralhepatitisprevention.aspx. Accessed November 20, 2012.

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Articles

WEB EXCLUSIVE

We examined the effect of country of origin on HBV testing and chronic HBV infection prevalence among APIs using data from Kaiser Permanente Hawaii.

Hepatitis B Virus Infection Testing and Prevalence Among Asian and Pacific Islanders

To examine the relationship between low-density lipoprotein cholesterol (LDL-C) goal attainment and adherence to statin medications in patients with coronary artery disease (CAD).

Cross-sectional study of CAD patients 18 years of age or older in an integrated healthcare system.

Patients dispensed 2 or more statin prescriptions between May 2009 and May 2010, were identified. Medication possession ratio (MPR) was calculated to estimate adherence. The LDL-C value closest to May 27, 2010, was used to determine goal. Adherence and LDL-C goal were defined as 80% or greater MPR and less than 100 mg/dL or less than 70 mg/dL, respectively. Electronic medical records were used to identify patient demographics and clinical information. Logistic regression was used to estimate the effect of these factors on goal attainment.

 A total of 67,100 CAD patients were identified. Overall, 85.8% had LDL-C less than 100 mg/dL, 32.4% had LDL less than 70 mg/dL, and 79.8% were adherent to their statin medication. Over 65% of patients not at LDL-C goal less than 100 mg/dL were adherent. Among patients with LDL-C less than 100 mg/dL, 17.9% were not adherent. Increasing medication adherence was associated with improved LDL-C levels. Adherence to statins, male sex, Asian and Hispanic race/ethnicity, a higher number of concurrent prescriptions, higher Charlson Comorbidity Index, and hypertension were associated with LDL-C goal attainment.

 Incorporating LDL-C levels and medication adherence at the point of care allows providers to focus interventions to address either adherence challenges or the need for medication titration in an effort to improve LDL-C goal attainment and ultimately reduce morbidity and mortality.

Low-density lipoprotein cholesterol (LDL-C) goal attainment is essential in coronary artery disease (CAD) patients to reduce morbidity and mortality of recurrent clinical events. We examined the association between LDL-C goal attainment and adherence to statins among CAD patients.

		Adherence to statin therapy was strongly associated with LDL-C goal attainment.

		Patients not at goal (<100 mg/dL) are more likely to be black (compared with white) and utilize more healthcare services such as the emergency department and inpatient stays.

		Many patients not adherent to their statin medication had LDL-C levels less than 100 mg/dL, suggesting that 80% adherence level may not be necessary to achieve this LDL-C goal.

Cardiovascular disease is the leading cause of death in the United States with 1 out of 5 deaths attributable specifically to coronary artery disease (CAD). Each year, approximately 635,000 people in the United States will have a new coronary attack and 280,000 will have a recurrence.

 Patients living with CAD have an increased risk of a recurrent coronary event,

which can lead to disability or death. In addition, in 2008, CAD had an estimated total national economic burden of $190.3 billion.

To reduce the risk of recurrent events, the National Cholesterol Education Program (NCEP) recommends a low-density lipoprotein cholesterol (LDL-C) goal level of less than 100 mg/dL for patients with CAD through the use of lipid-lowering drugs such as hydroxymethylglutaryl-CoA reductase inhibitors (statins),

 and studies have shown that those who have reached this goal have lower rates of recurrence and mortality.

 Despite their efficacy, the use of these drugs in CAD patients has been shown to be suboptimal.

Although studies have focused separately on LDL-C levels as well as adherence to statins among CAD patients, little is known about how adherence directly affects lipid levels in this population. We examined the association between LDL-C goal attainment and adherence to statins among CAD patients. By understanding the association of adherence and LDL-C goal attainment status, it will be possible to target the appropriate populations to ultimately improve patient treatment and outcomes.

This study was conducted at Kaiser Permanente Southern California (KPSC), an integrated healthcare system that provides care to approximately 3.5 million members across 10 counties in Southern California. The Institutional Review Board at KPSC approved this study.

The source population was identified from the KPSC CAD Registry, which identifies patients with CADrelated 

International Statistical Classification of Diseases, Ninth Revision, Clinical Modification

) diagnosis codes from hospitalization and outpatient and inpatient CAD-related 

 procedure codes (available upon request). The registry includes “risk equivalent” atherosclerotic cardiovascular diseases including ischemic stroke, peripheral arterial disease, and abdominal aortic aneurysm. Deceased members and those living in skilled nursing facilities are excluded from the registry.

Patients eligible for this study included all adult members 18 years or older in the registry as of May 27, 2010, with 12 months of continuous prior enrollment. Patients were included if they had filled at least 2 statin prescriptions (simvastatin or a combination of simvastatin and ezetimibe) and had at least 1 LDL-C result in this time period. Patients with rhabdomyolysis, creatinine kinase greater than 10,000 IU/L, allergy or intolerance to statins, and those without continuous membership or drug benefits between May 2009 and May 2010, were excluded. A total of 67,100 members were eligible and included in this analysis (

Information on patient demographics including age, gender, race/ethnicity, and clinical information including laboratory tests, prescription medications, healthcare utilization and comorbidities were extracted from health plan electronic databases. When multiple LDL-C measurements were present, the most recent measurement in the study period was extracted. LDL-C goal attainment was defined according to guidelines by the NCEP

 as less than 100 mg/dL and the optional goal of less than 70 mg/dL for these high-risk patients.

Information on healthcare utilization during the study period included the number of unique prescription medications (based on the generic product identified subclass), the numbers of clinic and emergency department (ED) visits and number of hospital admissions. The Deyo adaptation of the Charlson Comorbidity Index (CCI) was determined using 

diagnosis codes from inpatient and outpatient encounters as an overall measure of disease burden.

Pharmacy fill data were extracted for each eligible individual and a medication possession ratio (MPR) was calculated looking back 400 days from May 27, 2010, as the sum of the days of supply divided by the total number of days between the first dispense date and May 27, 2010, when the most recent prescription was overdue. If the most recent prescription was not overdue, then MPR was calculated as the sum of the days of supply, excluding the last refill, divided by the total number of days between the first dispense date and last dispense date. The most recent refill was disregarded when the prescription was not overdue to avoid inflating the MPR. Adherence was defined as MPR 80% or greater, as suggested by previous studies.

Descriptive statistics (means and proportions) were used to present patient characteristics categorized by LDLC goal attainment (<100 mg/dL; optional <70 mg/dL). Mean LDL-C levels were calculated according to various levels of adherence. Logistic regression was used to estimate the effect of adherence on LDL-C goal attainment controlling for multiple patient characteristics including adherence to statin medication, age, gender, race/ethnicity, concomitant medication use, utilization of healthcare services, CCI, and hypertension. All statistical analysis was performed using SAS version 9.2 (SAS Institute, Cary, North Carolina).

Characteristics of the study population stratified by LDL-C goal attainment are shown in 

. Nearly 86% of the population reached the LDL-C goal of less than 100 mg/dL; 32.4% reached the optional goal of LDL-C less than 70 mg/dL; and 79.8% of the population was adherent to their statin medication (MPR ≥80%). Patients at goal had higher adherence rates (LDL-C <100 mg/dL: 82.1% vs 66.0%; LDL-C <70 mg/dL: 84.3% vs 77.7%) and lower mean LDL-C values (LDL-C <100 mg/dL: 73.4 mg/ dL vs 121.0 mg/dL; LDL-C <70 mg/dL: 57.3 mg/dL vs 91.1 mg/dL) compared with those not at goal. Patients achieving LDL-C of less than 100 mg/dL and LDL-C less than 70 mg/dL were more likely to be taking lower-dose statins and more medications compared with those not at medicagoal. In general, patients at goal were also sicker, with a higher CCI score and higher percentages of diabetes, hypertension, and prior myocardial infarction (MI). Patients achieving LDL-C less than 100 mg/dL had slightly lower utilization of hospitalizations and ED visits in the past year while patients achieving LDL-C less than 70 mg/dL had higher utilization of hospitalizations and ED visits compared with those not at goal.

In this population of patients chronically taking statin medications, increasing adherence as determined by MPR was associated with improved LDL-C levels. LDL-C goal attainment had a positive association with increasing adherence, but goal attainment leveled off once adherence was over 86% (

). Men were consistently more adherent than women, and adherence increased with age (

Results of the multivariable logistic analysis are presented in 

. Patients who were adherent to their statin medication were much more likely to be at LDL-C goal than patients who were not adherent (LDL-C <100 mg/dL: odds ratio [OR], 2.24; 95% confidence interval [CI], 2.13-2.35; LDL-C <70 mg/dL: OR, 1.55; 95% CI, 1.48-1.62). Other patient characteristics associated with LDL-C goal attainment of less than 100 mg/dL included increased age, male sex, and Hispanic and Asian race/ethnicity (compared with whites). Patients prescribed more medications, and who had higher CCI scores, and who had hypertension, were more likely to have LDL-C less than 100 mg/dL. In contrast, black patients with CAD had lower odds of LDL-C less than 100 mg/dL compared with whites. Patients with more hospitalizations and ED visits in the prior year were also less likely to have LDLC of less 100 mg/dL. Results of the multivariable logistic regression analysis using LDL-C goal attainment of less than 70 mg/dL were similar although age and ED visits were no longer significantly associated with LDL-C goal attainment and patients who were hospitalized were more likely to be at LDL-C goal of less than 70 mg/dL.

In this study of patients with CAD in a large integrated healthcare system, 85.9% of patients were at the recommended LDL-C goal of less than 100 mg/dL and 79.8% were adherent to their statin medication. Among patients who were not at LDL-C goal, 66.0% were adherent while 17.9% of patients who were at goal were not adherent (defined as MPR <80%) to their statin medication. Adherence to statin medication, older age, male sex, Hispanic and Asian race/ethnicity, ambulatory utilization, higher CCI scores, and hypertension were associated with a higher likelihood of LDL-C goal attainment.

Overall, our study found higher rates of LDL-C goal attainment and secondary adherence to statin medications than many previous reports.

 However, we required 2 statin prescriptions during the study window in order to calculate the MPR, which may have resulted in a more adherent population. Additionally, there has been an ongoing effort within KPSC in recent years to achieve LDL-C goal levels in all patients through more stringent monitoring of medications and cholesterol levels. Healthcare providers offer medication therapy, education, and drug information to patients utilizing evidence-based guidelines, standardized practices, and tools to optimize pharmacologic efficacy and improve clinical outcomes. Providers are trained to identify barriers and offer solutions to help patients use medications correctly. In addition, patients who are overdue for refills or who have low adherence to taking certain medications receive telephone outreach. When looking at LDL-C goal attainment, Nag et al found that older age, hypertension, and a greater number of medication classes had a higher likelihood of goal attainment, which is in line with our findings.

 These results seem to suggest that patients who were older and had more comorbidities may have been more vigilant about taking medications due to increased perceived risk.

A substantial proportion of patients in our study who were not at goal were adherent, suggesting that these patients may have needed their medication adjusted. On the other hand, the patients who were not adherent and not at goal may have faced medication use and adherence challenges, and healthcare providers need to address those issues. Our finding that many patients not adherent to their statin medication had LDL-C levels less than 100 mg/dL warrants further investigation. It is possible that clinicians are titrating through nonadherence, although the design of our study did not allow us to analyze this specifically, or that an 80% adherence level to statin medication is not necessary to achieve optimal LDL-C levels. Throughout the literature, 80% has been the widely used cutoff to determine adherence to statin medications. This level of adherence has been associated with lower healthcare utilization,

 and it has been shown that patients with hypertension who are at least 80% adherent to antihypertensive therapy have better control of their blood pressure.

 However, studies suggest that an adherence rate of 80% is not necessarily the optimal cutoff point in every population.

 Karve et al looked at adherence to medications and its association with hospitalization in patients with schizophrenia, diabetes, hypertension, hyperlipidemia, and congestive heart failure. They determined that the optimal cutoff point that predicted disease-specific hospitalizations varied from 63% to 89%, depending on the disease.

 In a study involving heart failure patients, it was found that 88% was the level of adherence to all prescription medications needed to predict event-free survival. 

 Even low exposure—defined as medication possession between 1 and 365 days in a 2-year period&mdash;to a bundle of cardioprotective medications in a population of patients with diabetes and/or CAD was found to reduce risk of hospitalization for MI or stroke by 15 events per 1000 person-years.

 In our study, among patients who were at goal (LDL-C <100 mg/dL), 17.9% were not adherent to their statin medications, according the 80% cutoff point. Additionally, the mean LDL-C level in those with adherence rates as low as 26% to 35% was approximately 100 mg/dL. Though further studies are needed to determine what the most appropriate cutoff would be, these data suggest that it may be possible to achieve optimal LDL-C levels without 80% adherence to statin medications in CAD patients. Furthermore, in a setting of finite resources, it appears that focusing medication adherence efforts on patients who have adherence rates of 0% to 55% may have larger LDL improvement benefit than efforts focused on the group with greater than 55% adherence. The determination of an adequate cutoff value would be important for correctly identifying adherent and nonadherent patients, and implementing the most appropriate intervention.

There are several limitations to our study. We used pharmacy prescription refill information to calculate medication adherence, which indicates that a patient picked up their medication, but we do not know if they are actually taking the medication as prescribed. However, pharmacy fill data have been shown to be comparable to self-reports of adherence,25,26 and although pill splitting is a common phenomenon, it is typically more common with single-source medications due to cost. Furthermore, at the time of this study, simvastatin was generic; therefore, it was inexpensive and available at a generic copay for all strengths. It is possible that there was pill splitting with simvastatin-ezetimibe, but this would have been captured in the days of supply/daily dose fields in our pharmacy management information system. Additionally, only 15.1% of the patients with CAD were prescribed simvastatin-ezetimibe in this study, and we only included patients with drug benefits. Therefore, we do not believe pill splitting is a likely to have impacted the findings in this study. Patients could have been misclassified as nonadherent if they obtained their medications at a non-KPSC pharmacy, which would not be captured in our electronic systems. However, it is unlikely as more than 90% of KPSC members have a drug benefit that covers all or a portion of their medication costs, providing a financial incentive to use only KP pharmacies.

Furthermore, we excluded patients without pharmacy benefits. We did not assess the prescribing physician and patient relationship (physician specialty, inpatient, or outpatient setting), which also may have an influence on medication adherence. We included only 1 LDL-C value during the study period, and did not assess the length of time on the statin medication. We used MPR, one of the most common methods to estimate a patient’s adherence to medication using administrative databases. An inherent limitation to MPR is that it can only be calculated for patients with at least 2 dispenses. Thus, our study does not include the primary nonadherence population (those patients who did not fill their prescription the first time) and those who only filled the prescription once.

Lastly, the population we looked at had high adherence rates and high proportions at LDL-C goal. Given that KPSC is a managed care organization, our results may not be generalizable to other populations. However, the KPSC population has been shown to be generalizable to the Southern California population. The extremely poor and highly educated were only marginally underrepresented among KPSC members in 2010 compared with the general population living in Southern California. 

 Finally, this was a cross-sectional analysis, and we were not able to determine whether goal attainment or use of high dose statins has more benefit on clinical outcomes.

To our knowledge, the association between adherence to statin medications and LDL-C values has not been widely studied. Adherence was strongly associated with LDL-C goal attainment and LDL-C values. By examining LDL-C levels along with medication adherence levels, healthcare providers and health systems can focus their interventions to address either patient adherence challenges or the need for medication titration. The incorporation of effective point-of-care strategies—such as standardization and coordination of use of evidence-based guidelines, adherence tools embedded in the EMR, disease registries, and active outreach for the management of CAD patients&mdash;are vital for improving LDL-C goal attainment.

 Department of Research & Evaluation, Kaiser Permanente Southern California, Pasadena, CA (MDC, I-LAL, KR); Kaiser Permanente Pharmacy Analytical Service, Downey, CA (SSV, TCC); Kaiser Permanente, Southern California Permanente Medical Group, Pasadena, CA (KRG); West Los Angeles Medical Center, Southern California Kaiser Permanente, Los Angeles, CA (RDS).

 None reported. Author Disclosures: The authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

 Concept and design (SSV, I-LAL, TCC, KG, KR); acquisition of data (SSV); analysis and interpretation of data (MDC, I-LAL, TCC, KG, KR); drafting of the manuscript (MDC); critical revision of the manuscript for important intellectual content (MDC, I-LAL, TCC, KG, RDS, KR); statistical analysis (I-LAL, KR); administrative, technical, or logistic support (SSV, RDS); supervision (KR).

 Kristi Reynolds, PhD, MPH, Kaiser Permanente Southern California Department of Research & Evaluation, 100 S. Los Robles, 2nd Fl, Pasadena, CA 91101. E-mail: Kristi.Reynolds@kp.org.1. Go AS, Mozaffarian D, Roger VL, et al. Heart disease and stroke statistics--2013 update: a report from the American Heart Association. Circulation. 2013;127(1):e6-e245.

2. Rosamond W, Flegal K, Furie K, et al. Heart disease and stroke statistics--2008 update: a report from the American Heart Association Statistics Committee and Stroke Statistics Subcommittee. Circulation. 2008;117(4):e25-e146.

3. Wilt TJ, Bloomfield HE, MacDonald R, et al. Effectiveness of statin therapy in adults with coronary heart disease. Arch Intern Med. 2004; 164(13):1427-1436.

4. Wei L, Wang J, Thompson P, Wong S, Struthers AD, MacDonald TM. Adherence to statin treatment and readmission of patients after myocardial infarction: a six year follow up study. Heart. 2002;88(3):229-233.

5. Executive Summary of The Third Report of The National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, And Treatment of High Blood Cholesterol In Adults (Adult Treatment Panel III). JAMA. 2001;285(19):2486-2497.

6. Third report of the National Cholesterol Education Program (NCEP) expert panel on detection, evaluation, and treatment of high blood cholesterol in adults (adult treatment panel III) final report. Circulation. 2002;106(25):3143-3421.

7. Mills EJ, Rachlis B, Wu P, Devereaux PJ, Arora P, Perri D. Primary prevention of cardiovascular mortality and events with statin treatments: a network meta-analysis involving more than 65,000 patients. J Am Coll Cardiol. 2008;52(22):1769-1781.

8. Nishiwaki T, Kishi D, Yoshie F, et al. Relationship between LDL-C reduction after coronary revascularization and prevention of recurrence of cardiovascular events. J Pharm Pharm Sci. 2010;13(2):254-262.

9. Smith SC, Jr., Allen J, Blair SN, et al. AHA/ACC guidelines for secondary prevention for patients with coronary and other atherosclerotic vascular disease: 2006 update endorsed by the National Heart, Lung, and Blood Institute. J Am Coll Cardiol. 2006;47(10):2130-2139.

This study examined adherence to statins and low-density lipoprotein cholesterol goal attainment in patients with coronary artery disease.

Adherence to Statins and LDL-Cholesterol Goal Attainment

 Unhealthy behaviors are responsible for most chronic disease, ample healthcare costs, and around 40% of deaths. This study assessed patient satisfaction and behavioral outcomes for a program that enables healthcare settings to deliver evidence-based, cost-saving behavioral screening and intervention (BSI) services, as recommended by the US Preventive Services Task Force and others.

Thirty-one diverse primary care clinics and 2 other sites participated. Over 110,000 patients completed multibehavioral screening questionnaires. Those with positive alcohol or drug screens met with dedicated, on-site health educators for further assessment, and then received either reinforcement, brief intervention, or referral. Over 300 patients completed satisfaction questionnaires during year 3 of the program. A pseudo-randomly selected group of 675 patients participated in a 6-month follow-up telephone interview. In addition, for a short time in a pilot project at 3 clinics, 29 patients received depression screening, collaborative care, and behavioral activation, and 22 completed a 3-month follow-up telephone interview.

Mean patient satisfaction scores for all services exceeded 4.2 on a 5-point scale. Over 6 months, binge drinking episodes declined by over 20% for most subgroups. Recent marijuana use decreased by 15%. Depression symptom scores decreased by 55%.

With intensive training and ongoing support, cost-efficient paraprofessionals can deliver effective alcohol, drug, and depression screening and intervention services in busy healthcare settings. The approach holds promise for systematically addressing on a population-wide basis a variety of important behavioral health determinants and reducing related healthcare costs.

This article describes a program that successfully delivers evidence-based, costsaving alcohol, drug, and depression screening and intervention services in primary care settings.

Ample research has documented the effectiveness of these services and their return on investment, yet they are seldom delivered, largely because of provider and staff time pressures.

Trained and supported, cost-efficient paraprofessionals can deliver these services, elicit high patient satisfaction, and attain substantial declines in risky drinking, illicit drug use, and depressive symptoms.

Over time, clinical settings were able to modify work flow so that most patients received recommended services.

Several federal agencies and other authorities recommend that primary care providers conduct systematic tobacco, alcohol, depression, and obesity screening and intervention

: henceforth, behavioral screening and intervention (BSI). Dozens of randomized controlled trials and meta-analyses have demonstrated the efficacy of these services. Most such studies addressed 1 behavioral issue, involved selected consenting patients, and delivered services with more resources and tighter quality controls than most clinical settings could typically offer. Because of such studies, these services carry Grade A or B ratings from the US Preventive Services Task Force (USPSTF) and are consequently being advanced by the Affordable Care Act (ACA). Indeed, BSI could help reduce the $500 billion annual economic loss from tobacco use, alcohol use, and depression,

 as for every dollar spent, $4 is saved in 1 year through BSI for alcohol,

BSI starts with a few screening questions to identify patients who likely have behavioral risks or disorders.

 Positive screens require further assessment, which guides further service delivery, as per prior research. Low-risk drinkers or tobacco and alcohol abstainers receive reinforcement. Tobacco users and high-risk drinkers receive on-site interventions and ongoing support. Nicotine-dependent patients are offered pharmacotherapy. Patients with possible alcohol dependence, and patients with the desire but not the ability to modify tobacco or alcohol use, are referred for specialty care or other resources. In addition to usual pharmacotherapy and/or counseling, depressed patients receive collaborative care, in which typically a nurse or mental health professional coordinates management, promotes patient engagement in treatment during and in between visits, tracks depression symptom scores, and alerts treatment professionals to reconsider treatment plans if scores are not improving as expected.

 Collaborative care can also include behavioral activation, in which patients are engaged in behaviors that ameliorate depression symptoms.

Two major sets of barriers impede BSI implementation. Financial barriers are waning, as fee-for-service reimbursement for BSI is expanding under the ACA, and shared savings models are taking hold. Staff time is a more stubborn barrier. A primary care clinician would need 7.4 hours every work day to deliver all recommended preventive services, including BSI, leaving little time to address acute concerns and chronic illness.15 A potential solution is team-based care. Indeed, if the nation’s undersized primary care workforce will be able to serve the growing numbers of insured, elderly, and chronically ill patients, then primary care clinicians may need to delegate preventive services to other team members.

The US Substance Abuse and Mental Health Services Administration (SAMHSA) administers grants for demonstration projects to advance delivery of its alcohol and drug Screening, Brief Intervention, and Referral to Treatment (SBIRT) services. Successful projects develop a feasible service delivery model, serve a large number of patients, demonstrate patient satisfaction, and document reductions in substance use. Between 2006 and 2011, with SAMHSA grant funding, the Wisconsin Initiative to Promote Healthy Lifestyles (WIPHL) helped 33 clinical sites deliver SBIRT by expanding their healthcare teams with trained and supported “health educators” of varied backgrounds. WIPHL obtained separate funding to support delivery of depression screening and collaborative care for a short time at 3 sites. This paper reports on service delivery, patient satisfaction, and pre-/postintervention changes in behavioral outcome measures at the participating sites. The intent of this paper is to inform the design and implementation of programs that are intended to systematically identify and address multiple behavioral issues among large numbers of unselected patients, respect healthcare provider time constraints, help clinical settings meet several USPSTF recommendations, and serve the triple aim of improved health, patient experience, and cost control.

The Wisconsin Department of Health Services administered the project, contracted with the University of Wisconsin (UW) School of Medicine and Public Health and the Wisconsin Medical Society to administer a coordinating center, and contracted with the UW Population Health Institute to conduct program evaluation. SAMHSA provided funding and direction.

The Coordinating Center recruited sites through networking and selected them for their diversity in geographic location, population density (urban, suburban, and rural), payer mix, and their readiness to take advantage of the program. Sites were launched in waves to fill 18 slots. Additional sites were recruited as vacancies occurred. Initially, only primary care clinics were recruited. In later years, an emergency department (ED) and a hospital were added.

Interested sites completed a checklist to assess readiness and guide preparations. Sites instituted annual, universal screening for their entire patient population or for a subpopulation defined by provider or visit purpose. At most sites, receptionists distributed brief screening questionnaires. Sites were encouraged to include questions on multiple behavioral risks, in part to enhance acceptance of alcohol and drug questions.

 Staff members who checked vital signs reviewed questionnaire responses and referred patients with positive alcohol or drug screens to health educators. Consistent with SAMHSA requirements, health educators primarily conducted alcohol and drug assessment, then education, intervention, or referral as appropriate; they provided brief feedback and referral for other behavioral risks. They reported back to providers, who were encouraged to provide additional support and pharmacotherapy.

Sites were encouraged initially to identify champions, establish a quality improvement team with representatives from various staff segments, and design an initial work flow. The coordinating center provided consultation on best practices. Sites were encouraged to engage in monthly plan-do-study-act cycles to optimize 2 metrics: (1) the proportion of eligible patients who completed screens and (2) the proportion of patients with positive alcohol or drug screens who completed health educator— administered brief assessments.

The coordinating center guided each clinical site in hiring a health educator—a term that carries no legal implialcations in Wisconsin. Of the 44 health educators hired, 9 were master’s-level counselors or social workers, 33 held bachelor’s degrees, and 2 were high school graduates with special language or cross-cultural skills. Seven health educators had a bachelor’s degree in health education, 4 were certified health education specialists, and 1 was a certified chemical dependency counselor.

One week of distance training and a subsequent week of face-to-face training emphasized alcohol and drug screening, SBIRT, motivational interviewing, and cultural competence. Learning activities included interactive lectures, discussions, demonstrations, and role-play exercises, sometimes with standardized patients. Health educators had to pass a written examination and skills/ performance assessment. At their clinical sites, they received ongoing support through weekly conference calls and quarterly retreats. Monthly, with patients’ written consent, health educators submitted audiotapes of sessions and received structured feedback from trainers, who completed a modified skills checklist.

 Custom software guided health educators in realtime in delivering high-fidelity, evidence-based services. As they saw patients, health educators entered data, which allowed tracking of service delivery.

, the alcohol and drug screen included 3 validated questions on substance use

 and a direct question about illicit drug use or nonmedical use of prescription drugs. Questionnaires were available in several languages. For patients with positive screens, health educators administered the Alcohol, Smoking, and Substance Involvement Screening Test (ASSIST)

 plus 3 questions on quantity and frequency of alcohol use.

 Based on their responses, patients’ substance use was classified as low risk, risky, harmful, or likely dependent. Health educators asked additional questions as required by SAMHSA and the Governmental Performance Results Act.

For low-risk patients, health educators affirmed and reinforced low-risk behaviors. For patients with harmful or risky use, interventions ranged from brief feedback, recommendation, and negotiation to motivational interviewing. 

 Health educators varied their interventions in response to severity of risk or disorder, patient preference and response, and time constraints. For likely dependent patients, health educators recommended referral for specialized assessment or treatment. Most educators referred interested patients to a treatment liaison who was based at the coordinating center and gathered additional information by phone, provided encouragement and support, and negotiated referrals considering availability, financing, and patients’ needs and preferences. Grant funds were available to fund treatment for patients without other means.

Evaluation included follow-up telephone interviews 5 to 8 months after initial service delivery. Subjects included a randomly selected sample of patients who received interventions or referrals. The chief purpose of the interviews was to assess for changes in behavior. Questions on substance use were identical to those asked by health educators. Of 1099 patients eligible to participate, 874 (80%) provided consent, and 675 participated (77% of those consenting).

During the third project year, health educators were asked to distribute “patient satisfaction” surveys to all patients in 1 month. Patients anonymously submitted the surveys, which were based on the 4-item task subscale of the well-validated Likert-type Working Alliance Inventory, 

 which assesses the strength of psychotherapeutic relationships. The items, which originally focused on “problems” and “change,” were modified to focus on alcohol and drug use. The impact of these modifications on item validity is unknown.

Of the 33 participating clinics, 3 with bachelor’s-level health educators added the Patient Health Questionnaire-2 (PHQ-2) depression screen

 (Table 1) to their written questionnaires, and delivered BSI for depression in up to 4 sessions per patient. Their health educators attended 3 days of additional training involving lectures, role-play exercises, feedback, and coaching. For patients with positive screens, health educators administered the PHQ-9.

They referred patients with possible suicidality or major depressive episodes for further assessment and possible pharmacotherapy and/or psychotherapy. For patients with PHQ-9 scores of 10 or greater, they delivered collaborative care as guided by a workbook derived from protocols from several prior randomized controlled trials. Such care included education on etiology, treatment modalities, and treatment effectiveness, and motivational interventions to maximize adherence to pharmacotherapy and psychotherapy and to engage patients in behaviors that lift depressive symptoms, including exercise, sleep hygiene, socializing, enjoyable activities, healthy nutrition, and relaxation. At each subsequent visit, patients responded to the PHQ-9 and anonymously completed a brief satisfaction questionnaire. During 3-month follow-up telephone interviews, research assistants readministered the PHQ-9 and the satisfaction questionnaire.

The University of Wisconsin Human Subjects Committee exempted the main project from its purview, because it involved delivering evidence-based services and limited program evaluation to federal requirements. Nevertheless, health educators informed patients of the risks and benefits of participating, and patients gave written consent to participate in 6-month follow-up interviews. The Committee approved the research protocol for the depression pilot study.

Of 33 participating sites, 9 were located in or near Milwaukee, 10 in or near cities with populations of 30,000 to 250,000, and 14 in rural areas. The sites were 17 large commercial primary care or multispecialty groups, 6 federally qualified health centers or look-alikes, 5 independent commercial primary care sites, 2 tribal health centers, 1 health department clinic, 1 hospital-based ED, and 1 hospital trauma center, plus other inpatient units. Over the course of 51 months, average duration of participation was 25 months.

Between March 2007 and June 2011, 113,642 patients completed screens. Those screened were 63% female, 58% white and non-Hispanic, 15% Hispanic, 14% black or African American, 11% Native American, 1% Pacific Islander, and 2% multiple races. Of those, 15% were aged 18 to 24 years; 23% were aged 25 to 35 years; 50% were aged 36 to 64 years; and 12% were 65 years or older.

shows the flow of patients through the clinical process and the final categorization of patients’ substance use. Throughout the project, 68% of patients who were eligible completed a screen, and 63% of those with positive brief screens saw a health educator and completed a brief assessment. A plot of these metrics over time, as reported by health educators, is shown in

. A linear regression confirmed significant improvements over time for quarterly completion of screens (

 <.001). Metrics declined toward the end of the project, when new clinics replaced experienced clinics that had become self-sufficient and exited the grant-funded program.

 also shows the results of the brief assessments for patients who completed them. Assuming similarity between screened and unscreened patients, and between patients with positive screens who did and did not undergo brief assessment, the estimated population prevalence of abstinence or low-risk use was 73%; risky use, 22%; harmful use, 2%; and likely dependence, 3%.

Alcohol/Drug Intervention and Referral Services

All patients in the risky and harmful use categories received a brief intervention. All likely dependent patients were encouraged to accept a referral for specialized assessment or treatment. Of the 1855 likely dependent patients, 452 (24%) expressed initial interest in referral. Health educators’ and the treatment liaison’s records showed that 183 (10%) entered treatment, though data capture may have been incomplete.

We compared 675 patients’ initial, face-to-face selfreports to health educators with telephone self-reports provided 6 months later to evaluation staff. Results are shown in 

. Reductions in risky drinking, mean number of drinks per drinking day, and maximal number of drinks over the prior 3 months exceeded 20% and were consistent across most subgroups. Patients over age 65 years and patients with Medicare had lower base rates, and manifested small reductions. Bachelor’s-level health educators elicited greater reductions than those with master’s degrees (

Among those who screened positive for substance use, reports of past 3-month marijuana use declined from 28% at baseline to 23% at follow-up (

 <.001). Reports of past 30-day marijuana use declined from 23% to 19% (

 <.001). The low rates of other drug use precluded assessments of intervention effectiveness.

To assess for possible differences in the accuracy of self-reports of substance use to health educators at baseline and to evaluators at 6-month follow-up, we compared self-reports of lifetime substance use. Of the patients who provided complete information, 7 (1%) of 673 gave disparate reports of lifetime alcohol use, 4 reported use only to the evaluator, and 3 only to the health educator. Of the 128 with disparate reports of marijuana use, 85 (66%) reported use only to the evaluator, and 43 (34%) only to the health educator (

 <.0001). Of the 79 with disparate reports of cocaine use, 64 (68%) reported use only to the evaluator, and 25 (32%) only to the health educator (

Expanding primary care teams with trained and supported paraprofessionals enables systematic delivery of widely recommended, evidence-based, cost-saving alcohol, drug, and depression screening and intervention services.

A Team Approach to Systematic Behavioral Screening and Intervention

To quantify obesity inertia (OI) in primary healthcare in the Valencian Community (Spain) and determine the related factors.

 In 2003, the whole population 40 years and older was invited to undergo a check-up. We included all obese persons (body mass index [BMI] ≥30 kg/m

) of the first 6 months after the invitation (n = 8687). OI was defined as the lack of advice by the healthcare professionals to lose weight. Other data recorded: gender, history of cardiovascular risk factors (CVRFs) or cardiovascular disease (CVD), groups of BMI (Class I obesity [BMI <35 kg/m

] and the rest), age, blood pressure, and lipids. The patients without CVD and who were younger than 75 years (n = 7700) were classified according to the REGICOR cardiovascular risk as either high or low. The OI was quantified and related factors assessed, calculating the adjusted odds ratios (ORs) from multivariate models.

 In the overall sample, OI was 16.6% (95% confidence interval [CI], 15.8-17.4). Associated factors: male (OR = 1.19; 95% CI, 1.06-1.35); no history of hypertension (OR = 0.85; 95% CI, 0.74-0.97), or dyslipidaemia (OR = 0.86; 95% CI, 0.73-1.01), or diabetes (OR = 0.80; 95% CI, 0.64-1.00), or CVD (OR = 0.79; 95% CI, 0.62-1.01); and Class I obesity (OR = 0.83; 95% CI, 0.72-0.96). In the REGICOR sample, the OI was 16.9% (95% CI, 16.0-17.7). Associated factors: high REGICOR (OR = 2.27; 95% CI, 1.30-3.98) and Class I obesity (OR = 0.82; 95% CI, 0.71-0.95).

 OI exists in 1 of every 6 patients. OI occurs less frequently in patients with a history of CVRF, and more frequently in Class I obesity and in those with a high cardiovascular risk.

The cornerstone of treatment for obese patients is receiving advice about weight loss from a healthcare professional.

We evaluate the behaviour of these professionals when advising obese patients about diet and exercise.

Others have also analyzed this behaviour, assessing the lack of advice and its association with a history of cardiovascular risk factors.

We therefore calculated the cardiovascular risk and determined the association of this risk with the studied behavior.

A higher cardiovascular risk was associated with less advice received. n These results are important for improving the behavior of health professionals when treating an obese patient.

Obesity is a very common disorder worldwide.

In addition, obesity is associated with such healthcare problems as diabetes mellitus, hypertension, dyslipidaemia, and cardiovascular diseases (CVD).

 Clinical practice guidelines recommend that healthcare professionals intervene to reduce the prevalence of this problem. These professionals should help those patients who need to lose weight, by means of personalized counselling about a healthy lifestyle (food and physical exercise).

The Valencian Community is situated in the Mediterranean area of eastern Spain, and has a population of 4,518,126 inhabitants (figures from January 2004).

 Primary healthcare is given at health centers, and is universal and free for patients. The patients who attend these health centers are mainly women of older age with cardiovascular risk factors (CVRF), and they are frequent visitors.

 In this community, the Valencia study analyzed the impact of obesity in the population. From 1991 to 2005, the prevalence of obesity rose from 7.3% to 12.4%, and was the most important problem for men aged 34 years and older and women aged 50 years and older. Also of note was that these patients had other CVRFs.

In Spain, the health costs associated with obesity account for 7% of total healthcare costs. Over 35% of those costs correspond to obesity-related diseases such as CVD, diabetes mellitus, and dyslipidemia. The remaining 65% of costs are due to different types of cancer, kidney and liver disorders, sleep apnea, and even urinary incontinence—all related to obesity.

In obese patients, we assessed the lack of advice to lose weight and its association with cardiovascular risk using the REGICOR chart (adaption of Framingham).

April 2014

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