A profile of Latinos with poorly controlled diabetes in South Florida

JOURNAL OF COMMUNITY HOSPITAL INTERNAL MEDICINE PERSPECTIVES æ RESEARCH ARTICLE A profile of Latinos with poorly controlled diabetes in South Florida Sonjia Kenya, EdD, MS, MA1*, Cynthia Nicole Lebron, MPH2, Aileen Yu Hen Chang, MD1, Hua LI, PhD1, Yisel A. Alonzo, MA1 and Olveen Carrasquillo, MD, MPH1 1 Division of General Internal Medicine, University of Miami Miller School of Medicine, Miami, FL, USA; 2Jay Weiss Institute at Sylvester Cancer Center, University of Miami Miller School of Medicine, Miami, FL, USA Introduction: Latinos are the largest minority group in the United States and diabetes or pre-diabetes affects more than 70% of Latinos aged 45 years and older. Miami-Dade County is home to one of the highest populations of diverse Latinos. In this descriptive manuscript, we present baseline characteristics of participants enrolled in the Miami Healthy Heart Initiative (MHHI). This was a study conducted to determine the effects of a community health worker (CHW) intervention among Latinos with poorly controlled diabetes in South Florida. Methods: We recruited 300 diverse Latino adults with suboptimal diabetes outcomes (HbA1c ]8) into MHHI. This randomized control trial examined the impact of a 1-year CHW-led intervention on glycemic control, blood pressure, and cholesterol levels. At baseline, physiologic measures, including HbA1c, LDL, blood pressure, and BMI, were assessed. Data on socio-demographic characteristics and additional determinants of health such as depression status, provider communication, diet, exercise, cigarette smoking, readiness to change diabetes management behaviors (stages of change), and confidence in ability to improve diabetes selfcare (self-efficacy) were collected. Results: Participants came from 20 different countries, with Cuban Americans representing 38% of the sample. Most had lived in the US for more than 10 years, had completed at least 12 years of school, and had high levels of health literacy, yet 48% had very low acculturation. Nearly 80% had poor self-efficacy, 80% met the criteria for depression, and 83% were not adherent to their medications. More than half the population was not at their target for blood pressure, 50% were above the recommended LDL goal, and most were obese. Conclusion: In a diverse population of Latinos with poorly controlled diabetes in Miami, we found high rates of depression, obesity, medication non-adherence, poor self-efficacy, and provider communication. These may contribute to poor diabetes control, high blood pressure, and elevated cholesterol. Keywords: diabetes among Latinos; South Florida; diabetes disparities; Hispanics with diabetes *Correspondence to: Sonjia Kenya, Division of General Internal Medicine, University of Miami Miller School of Medicine, 1120 NW 14th Street, Room 968, Miami, FL 33136, USA, Email: skenya@med.miami.edu Received: 7 November 2014; Revised: 26 January 2015; Accepted: 3 February 2015; Published: 1 April 2015 atinos are the largest minority group in the United States (US) (1). By 2060, it is projected that nearly one in three persons in the US will be Latino (1). The incidence and prevalence of diabetes in this population is more than double that of non-Hispanic whites (2). Recent data from the Hispanic Community Health Study found that more than 70% of Hispanics aged 45 years and older have diabetes or pre-diabetes (3). With 64% of residents in Miami-Dade County being Latino (4), the county has one of the highest concentrations of Latinos in the US. In addition, with a rapid influx of Central and South L Americans, South Florida is also rapidly becoming one of the most diverse Latino populations in the country (5, 6). In this descriptive study, we provide an overview of a heterogeneous group of Latinos coming from a multitude of Latin American countries with poorly controlled diabetes, being cared for at the largest public hospital system in South Florida. We describe baseline characteristics of 300 Latino adults who were enrolled in a randomized study and provide data on physiologic measures, as well as socio-demographic, behavioral, and diabetes-specific constructs. Journal of Community Hospital Internal Medicine Perspectives 2015. # 2015 Sonjia Kenya et al. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), permitting all noncommercial use, distribution, and reproduction in any medium, provided the original work is properly cited. Citation: Journal of Community Hospital Internal Medicine Perspectives 2015, 5: 26586 - http://dx.doi.org/10.3402/jchimp.v5.26586 1 (page number not for citation purpose) Sonjia Kenya et al. Methods The Miami Healthy Heart Initiative (MHHI) is a National Institutes of Health/National Heart, Lung and Blood Institute
sponsored randomized clinical trial (R01 HL083857) examining the impact of a 1-year community health worker (CHW)
led intervention on glycemic control, blood pressure, and cholesterol levels among 300 Latinos with poorly controlled diabetes. Participants and measures The methodology for MHHI has been previously described (7). In brief, all participants were recruited from the primary care clinics of Jackson Health System which is in the Miami-Dade County public hospital system. Participants were identified by review of electronic health records and some by provider referral. Inclusion criteria included being between the ages of 30 and 60, having had diabetes for at least 6 months, and having their last hemoglobin A1c (HbA1c) done within the past year and being ]8 indicating poor glycemic control. Following study enrollment, participants completed a baseline intake that included systolic blood pressure (SBP) measured as per American Heart Association guidelines (8), phlebotomy for HbA1c and low-density lipoprotein (LDL) cholesterol determinations, body mass index, and a comprehensive 90-min patient interview including socio-demographic characteristics as well as behavioral and diabetesspecific measures collected using validated instruments (Table 1). Upon completion of these assessments, participants were randomized to a control or intervention group. Culturally relevant diabetes education materials were sent monthly to control group participants. Intervention group participants received 12 months of personalized support from a Latino CHW, who provided individualized diabetes management education, accompaniment to medical and social service appointments, and linkages to other relevant healthcare resources. Throughout the study, CHWs made an average of 5 home visits, 22 phone calls, and accompanied intervention participants to 1.28 clinic appointments. Data analysis Physiologic and questionnaire data were entered into a password protected Excel database. Data entry was reviewed for accuracy by a separate research assistant and exported into SPSS software, prior to analysis. Characteristics of the population involving categorical variables were examined using frequencies. Means and standard deviations were calculated for continuous variables. The study was approved by the University of Miami Institutional Review Board and is registered in clinical trials.gov (NCT01152957). Results Demographics The 300 Latino participants represented diverse Hispanic ethnicities. Persons born in Cuba made up approximately 38% of our sample and the rest came from a large variety of regions of Latin America, including at least 10 persons born in Nicaragua, Colombia, Dominican Republic, Puerto Rico, Peru, and the mainland United States. The others were from a large variety of countries, including Puerto Rico, Mexico, Dominican Republic, Ecuador, Columbia, Guatemala, Peru, Brazil, Argentina, Venezuela, and Honduras. Most participants (80%) had been living in the US for more than 10 years. With respect to race, four-fifths self-identified themselves as White (81%); 16% indicated their race was either ‘moreno’, ‘mixto’, ‘indigeno’, or another variation; and 3% identified their race as Black (Table 2). Acculturation level, the process of adapting to a new culture, was assessed using the Marin Short Acculturation Scale (MSAS) (10), which primarily focuses on the linguistic components of acculturation. The MSAS has six questions using a Likert scale and respondents can have a total acculturation score ranging from 6 to 30. The total acculturation score was calculated for each participant. Then, based on frequency distributions, we grouped respondents into acculturation tertiles to indicate minimal acculturation, low acculturation, or moderate acculturation. The lowest possible score was 6, and participants who met that criteria were categorized as minimally acculturated. Acculturation status among those who scored between 7 and 10 was categorized as low, and those who scored between 11 and 30 met the criteria for moderate. Most of the sample scored minimal on acculturation with 48% achieving the lowest score possible and less than a quarter meeting the criteria for moderate acculturation. However, nearly 60% had completed at least 12 years of schooling (usually in their home country). Health literacy, which was measured using the Spanish version of the Short Assessment of Health Literacy Spanish and English (SAHL S&E), was relatively high with 85% having adequate health literacy, an understanding of common medical terminology. Physiologic measures To be eligible for the study, participants had to have their diabetes under relatively poor control with their last HbA1c being ]8. During our review of the electronic medical record, which was completed when identifying potential participants for study inclusion, the mean HbA1c among the sample was 9.31. However, at the time participants had their initial measurement for MHHI, usually done a few months after the initial HbA1c, the mean A1C had dropped to 9.13. With respect to blood pressure, the mean SBP was 133 mmHg919. Based on existing ADA criteria at the 2 Citation: Journal of Community Hospital Internal Medicine Perspectives 2015, 5: 26586 - http://dx.doi.org/10.3402/jchimp.v5.26586 (page number not for citation purpose) Latinos with poorly controlled diabetes in South Florida Table 1. Validated Assessments administered at baseline Measures Scale name Description Socio-demographic Health literacy Acculturation Short Assessment of Health Literacy This scale uses 18 items to assess participant’s understanding of Spanish and English (SAHLS&E) (9) common medical terminology. Marin Short Acculturation Scale (10) This 12-item scale was developed for Hispanics highly correlated with length of US residency, age at arrival, ethnic self-identification, and respondent’s generation. Behavioral Depression European depression-D (11) This 12-item EURO-D scale asks participants about factors such as appetite, tearfulness, irritability, and trouble sleeping, among Alcohol intake Alcohol Use Disorders Identification This three item screening instrument is used to help providers Test (AUDIT-C) (12) identify patients who are hazardous drinkers or have active alcohol others. Response categories are dichotomous. use disorders. Cigarette smoking Diet Physical activity Participants were asked if they currently smoke. Behavioral Risk Factor Surveillance The dietary intake section was used to measure the number of System: Fruit and Vegetable Intake (13) fruits and vegetables they consume daily as well as how many times a week they consume breakfast. International Physical Activity This 4-item scale assesses the time spent doing moderate or Questionnaire (I-PAQ) (14) vigorous activity, walking, or sitting. Participants are then place in low, medium or high category. Health measures Adherence to medication Morisky Medication Adherence Scale (15) This 8-item scale addresses adherence issues like forgetfulness or discontinuing medication because it makes patients feel better or worse. Response categories are yes/no for each item with a dichotomous response. Behavioral change Stages of Change (16) Participants’ response placed them in one of the following categories: precontemplation, contemplation, preparation, action, or maintenance. Diabetes self-efficacy The Diabetes Distress Scale (17) This 2-item scale measures two potential problem areas for people Provider communication Medical Care Scale from the Stanford The scale consists of three measures with responses on a Patient Education Center (18) five-point gradient scale. Scale items address preparation for clinic appointments and discussions of confusion and personal living with diabetes. The items are on a 6 point gradient scale. problems related to patient’s illness. After the mean and SD were calculated for the health measures above, the variables were recoded to reduce responses into more meaningful categories. For example, provider communication was recoded into a dichotomous variable. ‘Sometimes, almost never, or never’ were combined into a single category and ‘fairly often, very often, and always’ were also combined. In addition, the stages of change measure includes five response categories (precontemplation, contemplation, preparation, action, and maintenance) and we reduced the response categories by combining the precontemplation and contemplation phases into one response category and action and maintenance into another response category, and preparation remained a single category, reducing the potential responses into three possibilities. time of the study, the target blood pressure was under 130 (19), which meant that more than half the population was not at their target for blood pressure. Using the newer criteria of SBP B140 (19), almost a third of participants were not at goal. In addition, mean LDL was 105940, indicating that 52% were above the existing ADA goal of LDLB100 (9). The population was also predominantly obese with a mean body mass index (BMI) of 3297 (BMI ]30 is considered obese). Behavioral measures With respect to behavioral measures, the most notable finding was depression. Using the EURO-D cut-off of 3 or greater, 80% of participants screened positive for depression and more than half had scores of five or greater indicating high levels of mental health impairment. Other behavioral measures were also notable including 55% of the patients being sedentary and on average consuming only two servings of fruits and vegetables per day. Though Citation: Journal of Community Hospital Internal Medicine Perspectives 2015, 5: 26586 - http://dx.doi.org/10.3402/jchimp.v5.26586 3 (page number not for citation purpose) Sonjia Kenya et al. Table 2. Demographics and health outcomes Demographics N (%) Sex Male Female Age 135 (45) 165 (55) 55.2597.02 Race White Black Other Refused to answer 242 (80.5) 8 (3) 47 (15.5) 3 (1) Adequate Health Literacy 255 (85) Income B$1,200 per month 127 (49) % Uninsured Physiologic measures HbA1c 81% Mean (SD) 9.31 (1.99) LDL 105.01 (39.67) Systolic blood pressure Diastolic blood pressure 133.00 (18.92) 77.14 (10.24) BMI 32.26 (7.41) this is well below national recommendations, these rates mirror the national rates of fruit and vegetable consumption (20). Some findings were encouraging. For example, most participants ate breakfast more than 5 days per week. In addition, hazardous alcohol use was less than 190 3% versus the national rate of 7% (21). Only 15% of participants were current smokers, compared to national rates of 18% (22). Diabetes-related measures Another problematic area was with respect to medication adherence. Using the Morisky Medication Adherence (15) scale, a score of B6 was classified as low adherence, a score of 6 to 7 was considered medium adherence, and a score of 8 was considered high adherence. In our population, only 16% had high adherence to their medications. Self-efficacy scores, as measured by the brief diabetes distress scale (DDS 2), also indicated another problem area. The DDS 2 is a diabetes distress screening instrument asking respondents to rate on a 6-point scale, the degree to which the following items caused distress: 1) feeling overwhelmed by the demands of living with diabetes and 2) feeling that, ‘I am often failing with my diabetes regimen’. A DDS 2 score of ]3 indicates a moderate or more severe problem. DDS 2 scores are tabulated by adding the numbers (1 through 6) assigned to each response category in the Likert scale and then dividing the sum by 2 (i.e., 8/2 4). Nearly 80% of our sample met the criteria for moderate to serious distress, as indicated by the sample’s mean score of 4.06 (Table 3). Encouraging were our findings with respect to stages of change with 70% of the sample being in the preparation, action, or maintenance phase. Another area for opportunity was with respect to communication with providers. More than a quarter of these patients with poorly controlled diabetes indicated that they ‘never’ or ‘almost never’ communicated with their provider about health issues and just 10% communicated with their provider ‘very often’ or ‘always’ regarding their health concerns. Lastly, we examined whether any of these characteristics, including depression, self-efficacy, readiness to change (stages of change), and medication adherence, were correlated with A1c. However, in bivariate analysis we did not find any significant correlations with A1c. Though, a major limitation of this approach is that all participants had poorly controlled diabetes, a more appropriate strategy would have been to compare the distribution of characteristics among MHHI participants with another sample of people with well-controlled diabetes. Discussion In this manuscript, we describe a heterogeneous population of Latinos from various countries of origin in Miami with poorly controlled diabetes. We found that despite having poor diabetes control and being predominantly obese, two-thirds of our sample had their blood pressure controlled as per the revised ADA guidelines, and half were at the existing target for LDL control. Although there is still considerable room for improvement, these metrics from a public hospital low income population are similar to NHANES data among a nationally representative sample, which shows that 53% of a nationally representative sample had poor blood pressure in New York City (]130/80) and 41% had poor total cholesterol (]200) (23). When these findings were discussed with several of our providers, they were also surprised, as they expected a higher proportion of participants to have poorly controlled blood pressure and cholesterol. Though our findings on fruit and vegetable consumption and physical activity are not different from the general population, these areas warrant improvement and demonstrate specific opportunities for behavioral intervention. The most concerning findings were with respect to depression. Several other studies (24, 25), including our own prior research of Latinos in the northeast (26), have also found high rates of depression among those with diabetes. However, in our study, 80% of the population screened positive for depression, which is much higher than the 52% we previously reported (25) and the 30% cited in other studies. Previous research has shown that depressed individuals with diabetes have medical costs nearly five times greater than those without depression and that co-treating both conditions can decrease healthcare costs (27
29). Several approaches, such as the collaborative care model, have been shown to successfully treat both depression and glucose management (27
29). Our data again reiterate the importance of assessing depression 4 Citation: Journal of Community Hospital Internal Medicine Perspectives 2015, 5: 26586 - http://dx.doi.org/10.3402/jchimp.v5.26586 (page number not for citation purpose) Latinos with poorly controlled diabetes in South Florida Table 3. Health status indicators Scales Health care behaviors Mean (SD) Categorical (dichotomous) Communication with doctor 2.14 (1.13) 62% never, almost never, or sometimes Range 1 (never) through 5 (always) Morisky medication adherence 5.65 (1.75) communicated with PCP 83% did not meet criteria for adherent 5.62 (3.2) 80% met criteria for depression Diabetes distress 4.06 (1.52) 79% met criteria for moderate to serious distress Range 1 (not distressed) through 6 (distressed) Positive competence 2.84 (0.41) 42% (as measured by Diabetes Self-Efficacy Scale) 2.77 (0.47) 92% (as measured by Diabetes Self-Efficacy Scale) Range 0 (non-adherence) through 8 (adherent) Psychological measures Depression Range 0 (not depressed) through 12 (depressed) Range 1 (low self-efficacy) through 4 (high self-efficacy) Negative dietary competence Range 1 (high dietary self-efficacy through 4 (low dietary self-efficacy) Stages of change 1. Precontemplation or contemplation 2. Preparation 3. Action or maintenance Health behaviors N (%) 92 (30) 92 (31) 116 (39) Mean (SD) Daily fruit consumption 1.13 (1.0) Daily vegetable consumption 1.40 (1.0) Weekly breakfast consumption 5.68 (2.4) IPAQ physical measurement Low Moderate N (%) 166 (55) 77 (26) High 57 (19) Smoking 45 (15) Hazardous alcohol 38 (13) among patients with diabetes and linking depressed patients with effective treatments. We also found that 83% of our patients did not have appropriate medication adherence. In addition, self-efficacy scores suggested that many participants felt overwhelmed or that they were failing in diabetes management. Prior studies have shown that improving selfefficacy may lead to better glycemic control (30). One strategy to address these barriers is the use of motivational interviewing, which has been shown as an effective tool, to improve medication adherence among people with diabetes and other chronic conditions (31, 32). Because most patients also reported difficulties with provider communication, this is an additional area warranting attention. However, we also found that, with respect to stages of change, 40% of our sample met the criteria to be in the action or maintenance phase. This finding is supported by the fact that even prior to enrollment in the study, the mean HbA1c of our patients improved, suggesting that many participants were at least taking some action to control their diabetes. Voluntarily enrolling into a clinical trial focused on improving diabetes management may also indicate progression along the stages of change continuum and readiness to change their diabetes self-care behaviors. Among the strengths of our study was a highly diverse Latinos population. However, our sample was from South Florida and may not be generalized to Latino populations in other geographic regions of the country, which tend to be more homogenous in ethnicity. Second, as only poorly controlled diabetics were enrolled into our study, the findings reported here are not representative of the national population of patients with diabetes, or all Latinos with diabetes. Estimates are that about half the patients with diabetes have HbA1c B7.0 (33). Lastly, there are numerous planned future manuscripts based on our data, including the impact of the intervention on diabetes, blood pressure, and LDL. Other manuscripts will examine data on acculturation on outcomes and health care utilization. Citation: Journal of Community Hospital Internal Medicine Perspectives 2015, 5: 26586 - http://dx.doi.org/10.3402/jchimp.v5.26586 5 (page number not for citation purpose) Sonjia Kenya et al. In summary, we found that in a large sample of heterogeneous Latinos with poorly controlled diabetes and obesity in Miami, levels of blood pressure and cholesterol control were similar to other studies of less impaired patients with diabetes. Many behavioral measures such as diet, exercise, smoking, and alcohol also mirrored data from non-diabetics. However, there were several areas that warrant attention, including high rates of depression, medication non-adherence, and provider communication. These are all important domains to consider in improving care delivery to this vulnerable population with poorly controlled diabetes. Acknowledgements This research was supported by an award from NIH/NHLBI R01 HL083857. Conflict of interest and funding The authors have not received any funding or benefits from industry or elsewhere to conduct this study. References 1. Centers for Disease Control. Hispanic or Latino populations. Available from: http://www.cdc.gov/minorityhealth/populations/ REMP/hispanic.html [cited 19 August 2014]. 2. Pleis JR, Lucas JW. Summary health statistics for U.S. adults: National Health Interview Survey, 2007. National Center for Health Statistics. Vital Health Stat 2009; 10: 6
31. 3. U.S. Department of Health and Human Services, National Institutes of Health, National Heart, Lung, and Blood Institute (2013). Hispanic Community Health Study/Study of Latinos Data Book: A report to the communities. Bethesda, MD: U.S. Department of Health and Human Services, National Institutes of Health, National Heart, Lung, and Blood Institute. NIH Publication No. 13-7951. 4. U.S. Census Bureau. State and County QuickFacts: MiamiDade County, FL. Available from: http://quickfacts.census.gov/ qfd/states/12/12086.html [cited 19 August 2014]. 5. Stoney S, Batalova J. Central Americans in the United States. Migration Policy Institute; 2013. Available from: http://www. migrationpolicy.org/article/central-american-immigrants-unitedstates [cited 19 August 2014]. 6. Stoney S, Batalova J, Russell J. South Americans in the United States. Migration Policy Institute; 2013. Available from: http://www.migrationpolicy.org/article/south-american-immigrantsunited-states [cited 19 August 2014]. 7. Carrasquillo O, Patberg E, Alonzo Y, Li H, Kenya S. Rationale and design of the Miami Healthy Heart Initiative: A randomized controlled study of a community health worker intervention among Latino patients with poorly controlled diabetes. Int J Gen Med 2014; 7: 115
26. 8. Pickering TG, Hall JE, Appel LJ, Falkner BE, Graves J, Hill MN, et al. Recommendations for blood pressure measurement in humans and experimental animals: Part 1: Blood pressure measurement in humans: A statement for professionals from the Subcommittee of Professional and Public Education of the American Heart Association Council on High Blood Pressure Research. Hypertension 2005; 45: 142
61. 9. Lee SY, Bender DE, Ruiz RE, Cho YI. Development of an easyto-use Spanish Health Literacy test. Health Serv Res. 2006; 41: 1392
412. 10. Marin G, Sabogal F, Marin BV, Otero-Sabogal R, Perez-Stable EJ. Development of a short acculturation scale for Hispanics. Hispanic J Behav Sci 1987; 9: 183
205. 11. Prince MJ, Reischies F, Beekman AT, Fuhrer R, Jonker C, Kivela SL, et al. Development of the EURO-D scale
A European, Union initiative to compare symptoms of depression in 14 European centres. Br J Psychiat. 1999; 174: 330
8. 12. Bush K, Kivlahan DR, McDonell MB, Fihn SD, Bradley KA. The AUDIT alcohol consumption questions (AUDIT-C) an effective brief screening test for problem drinking. Arch Intern Med 1998; 158(16): 1789
95. 13. Serdula M, Coates R, Byers T, Mokdad A, Jewell S, Chávez N, et al. Evaluation of a brief telephone questionnaire to estimate fruit and vegetable consumption in diverse study populations. Epidemiology 1993; 4(5): 455
63. 14. Craig CL, Marshall AL, Sjö Strö M, Bauman AE, Booth ML, Ainsworth BE, et al. International physical activity questionnaire: 12-country reliability and validity. Med Sci Sports Exerc 2003; 35(8): 1381
95. 15. Morisky DE, Green LW, Levine DM. Concurrent and predictive validity of a self-reported measure of medication adherence. Med Care 1986; 24(1): 67
74. 16. Prochaska JO, Velicer WF. The transtheoretical model of health behavior change. Am J Health Promot 1997; 12(1): 38
48. 17. Polonsky WH, Fisher L, Earles J, Dudl RJ, Lees J, Mullan J, et al. Assessing psychosocial distress in diabetes development of the diabetes distress scale. Diabetes Care 2005; 28(3): 626
31. 18. Lorig K, Stewart A, Ritter P, González V, Laurent D, Lynch J. Outcome measures for health education and other health care interventions. Thousand Oaks, CA: Sage; 1996, pp. 24
40. 19. American Diabetes Association. Standards of medical care in diabetes
2013. Diabetes Care 2013; 36(1): S11
66. 20. Centers for Disease Control and Prevention (2013). State indicator report on fruits and vegetables, 2013. Atlanta, GA: Centers for Disease Control and Prevention, U.S. Department of Health and Human Services. 21. Substance Abuse and Mental Health Services Administration (SAMHSA). 2012 National Survey on Drug Use and Health (NSDUH). Available from: http://www.samhsa.gov/ data/NSDUH/2012SummNatFindDetTables/DetTabs/NSDUHDetTabsSect5peTabs1to56-2012.htm#Tab5.8A [cited 9 October 2014]. 22. Centers for Disease Control and Prevention. Adult cigarette smoking in the United States: Current Estimates. Available from: http://www.cdc.gov/tobacco/data_statistics/fact_sheets/adult_data/ cig_smoking/ 23. Chatterji P, Joo H, Lahiri K. Racial/ethnic- and educationrelated disparities in the control of risk factors for cardiovascular disease among individuals with diabetes. Diabetes Care 2012; 35(2): 305
12. 24. Gonzalez HM, Haan MN, Hinton L. Acculturation and the prevalence of depression in older Mexican Americans: Baseline results of the Sacramento Area Latino Study on Aging. J Am Geriatr Soc 2001; 49(7): 948
53. 25. March D, Luchsinger JA, Teresi JA, Eimicke JP, Findley SE, Carrasquillo O, et al. High rates of depressive symptoms in low-income urban Hispanics of Caribbean origin with poorly controlled diabetes: Correlates and risk factors. J Health Care Poor U. 2014; 25(1): 321
31. 26. Gross R, Olfson M, Gameroff MJ, Carrasquillo O, Feder A, Lantigua RA, et al. Depression and glycemic control in primary care patients with diabetes. J Gen Int Med 2005; 20: 460
6. 6 Citation: Journal of Community Hospital Internal Medicine Perspectives 2015, 5: 26586 - http://dx.doi.org/10.3402/jchimp.v5.26586 (page number not for citation purpose) Latinos with poorly controlled diabetes in South Florida 27. Hay JW, Katon WJ, Ell K, Lee P, Guterman JJ. Costeffectiveness analysis of collaborative care management of major depression among low-income, predominantly Hispanics with diabetes. Value Health 2012; 15(2): 249
54. 28. Katon W, Russo J, Lin EB, Schmittdiel J, Ciechanowski P, Ludman E, et al. Cost-effectiveness of a multicondition collaborative care intervention: A randomized controlled trial. Arch Gen Psychiatry 2012; 69(5): 506
14. 29. Ell K, Katon W, Xie B, Lee PJ, Kapetanovic S, Guterman J. Collaborative care management of major depression among low-income, predominantly Hispanics with diabetes: A randomized controlled trial. Diabetes Care 2010; 33: 706ialw. 30. Sarkar U, Fisher L, Schillinger D. Is self-efficacy associated with diabetes self-management across race/ethnicity and health literacy? Diabetes Care 2006; 29(4): 823
9. 31. Schmaling KB, Blume AW, Afari N. A randomized controlled pilot study of motivational interviewing to change attitudes about adherence to medications for asthma. J Clin Psychol Med Settings 2001; 8(3): 167
72. 32. Dilorio C, Resnicow K, McDonnell M, Soet J, McCarty F, Yeager K. Using motivational interviewing to promote adherence to antiretroviral medications: A pilot study. J Assoc Nurses AIDS Care 2003; 14: 52
62. 33. Vouri SM, Shaw RF, Waterbury NV, Egge JA, Alexander B. Prevalence of achievement of A1c, blood pressure, and cholesterol (ABC) goal in veterans with diabetes. J Manage Care Pharm 2011; 17(4): 304
12. Citation: Journal of Community Hospital Internal Medicine Perspectives 2015, 5: 26586 - http://dx.doi.org/10.3402/jchimp.v5.26586 7 (page number not for citation purpose)