Sex Disparities in Control and Treatment of Modifiable Cardiovascular Disease Risk Factors Among Patients With Diabetes

RESEARCH DESIGN AND METHODS—

The TRIAD Study methods have been described previously (11). The primary objective of the TRIAD Study is to determine how the structural and organizational characteristics of health systems and health care provider groups influence processes and outcomes of diabetes care (12). Six translational research centers collaborate with 10 health plans and 68 provider groups, which serve ∼180,000 patients with diabetes. Health plans from Hawaii, California, Texas, Indiana, Michigan, New Jersey, and Pennsylvania are represented and include a racially and ethnically diverse membership. The study protocol was approved by the institutional review boards at all six translational research centers.

The TRIAD Study population consisted of a random sample of adult enrollees with diabetes from the participating health plans. Patients were eligible for the TRIAD Study if they were aged ≥18 years, were community dwelling, were English or Spanish speaking, were not pregnant, had diabetes for at least 1 year, were continuously enrolled in the health plan for at least 18 months, and used services during that time.

Diabetes diagnosis was based on data from the year before enrollment and included one or more of the following criteria: at least two outpatient visits or one inpatient stay with a diagnostic code for diabetes (ICD-9 250.xx) or laboratory tests or values suggesting diabetes (at least two A1C tests ordered or a diagnostic A1C or fasting blood glucose level) or a prescription for medications for diabetes (for example, insulin or an oral antidiabetic agent). At the time of the survey, patients who met these initial criteria were included only if they verified that they had diabetes and received most of their diabetes care through the participating TRIAD health plan. All of the participants provided informed consent.

Data sources

Recruitment was completed in September 2001. Patient surveys included questions on health status, diabetes duration, current diabetes treatment, and demographic characteristics. Of the 13,086 individuals who were contacted and eligible, 11,927 (91%) completed the survey (56.6% by computer-assisted telephone interview and 43.4% by written survey). If we assume that the individuals whom we could not contact had the same rate of eligibility as those whom we contacted, the response rate as endorsed by the Council of American Survey Research Organizations (13) was 69%.

Of the patients completing a survey, 73% consented to medical record review and subsequently had charts available for review; the participants whose charts were reviewed were similar to the overall study population (14). Centrally trained reviewers used standardized data collection software to abstract levels of modifiable CVD risk factors (see below) and medications during the 12 months before the survey date. Five percent of records were abstracted in a double-blind fashion; that is, reviewers were not aware of which subjects were selected for double abstraction. Inter-rater reliability (κ) for the main quality measures derived from medical record data ranged from 0.86 to 0.94.

Study end points included levels of modifiable CVD risk factors such as the most recent levels of SBP, LDL cholesterol, and A1C. These outcomes were analyzed as binary variables (≥140 vs. <140 mm Hg for SBP, ≥3.35 vs. < 3.35 mmol/l for LDL cholesterol, and ≥8.0 vs. < 8.0% for A1C) according to the levels considered to be not in control and that therefore require more action as recommended at that time by the American Diabetes Association (15). We defined a second set of end points to reflect the intensity of medication management strategies of the three outcomes for individuals with risk factor values at or above these cut points. For each CVD risk factor, we calculated the sex-specific proportion of the patients with levels not in control who were currently receiving more intensive medication management, presumably reflecting a greater effort to manage the outcome (16). More intense medication management was operationalized as the use of two or more drug classes of antihypertensive agents for hypertension, of one or more lipid-lowering agents for hypercholesterolemia, and of two or more oral agents or insulin for diabetes.

The covariates were obtained from the patient survey: age, sex, race/ethnicity, education, income, BMI, smoking, duration of diabetes, and a four-level treatment variable (diet-controlled, oral agents only, oral agents and insulin, or insulin alone). History of CVD was defined according to self-reported myocardial infarction, stroke, coronary artery bypass, or angioplasty.

Statistical methods

We used hierarchical logistic regression models (SAS GLIMMIX Macro with penalized quasi-likelihood-estimation method) with random intercepts for health plan to account for the multilevel study design (health plan, provider group, and patient levels). Our goal was to estimate population-level differences in the levels and treatment of CVD risk factors by sex. We used hierarchical logistic regression to model the probability of having CVD risk factors not in control or of receiving more intense medication treatment for those with poorly controlled CVD risk factors. We then modeled the risk differences between men and women and their 95% CIs. A1C, LDL cholesterol, and SBP values were unavailable for 8, 24, and 5% of the patients, respectively; these patients were excluded only from analyses of the missing end point. We first present the unadjusted estimated probabilities of having modifiable CVD risk factors not in control or receiving more intense medication treatment because management of CVD risk factors in patients with diabetes are normative standards of quality of care that are relevant for all of the patients, regardless of demographics or clinical characteristics (15,17). We also explored whether demographic and clinical characteristics (such as age, race/ethnicity, education, income, BMI, time since diabetes diagnosis, hypoglycemic therapy, and current smoking) explained observed sex differences. Because women were much less likely than men to have a history of CVD (27.8 vs. 38.5%), therefore resulting in possible confounding between sex and history of CVD, the analyses were stratified by history of CVD.

RESULTS—

There were 1,314 women and 1,575 men with a history of CVD and 3,415 women and 2,516 men without a history of CVD. Within each stratum, women were older than men and were more likely to come from U.S. minority racial/ethnic groups, to report lower education and income levels, to have a longer diabetes duration, to use insulin alone or in combination with hypoglycemic oral agents, and to have a higher BMI (Table 1).

Among patients with a history of CVD, women were significantly more likely than men to have SBP ≥140 mmHg and LDL cholesterol ≥3.35 mmol/l in both unadjusted models and models adjusted for covariates (all P values < 0.01). No differences in the estimated probability of having A1C levels not in control were observed between men and women with a history of CVD (Table 2).

Among patients without a history of CVD, women were significantly more likely to have SBP ≥140 mmHg than men in unadjusted analysis (P = 0.04); this observed sex difference in SBP control was largely reduced and no longer significant after adjustment for covariates. Men and women without a history of CVD had similar estimated probabilities for having LDL cholesterol or A1C levels not in control (Table 2).

Table 3 reports the intensity of medication management among patients with levels of CVD risk factors not in control. Among those with a history of CVD, the estimated probabilities of receiving lipid-lowering medication if LDL cholesterol levels were ≥3.35 mmol/l were lower in women than in men, although the risk differences between men and women were not significant in either the unadjusted analysis (risk difference = 6.7%; P = 0.12) or the adjusted analysis (risk difference = 9.1%; P = 0.06). The estimated probabilities of receiving two or more antihypertensive medications if SBP was ≥140 mmHg or two or more diabetes medications if A1C was ≥8.0% were similar in men and women with a history of CVD.

Among patients without a history of CVD, women were significantly more likely to receive two or more antihypertensive medications than men if SBP was ≥140 mmHg (P < 0.01), although this difference was no longer significant after adjustment for covariates (P = 0.10). Medication management intensities for LDL cholesterol levels ≥3.35 mmol/l and A1C levels ≥8.0% were similar in men and women without a history of CVD (Table 3). Of note, both men and women with a history of CVD were more likely to receive more intense medication management for hypertension or hypercholesterolemia than men and women without a history of CVD, whereas intensity of medication management for diabetes was similar in patients with and without a history of CVD (Table 3).

CONCLUSIONS—

In this insured population of patients with diabetes from several managed care organizations in the U.S., we saw several sex differences in risk factor control and management. In patients with a history of CVD, women were more likely than men to have uncontrolled levels of both SBP and LDL cholesterol; they were also somewhat less likely than men to be receiving more intensive medication management when LDL cholesterol levels were not in control. These differences among patients with a history of CVD were not explained by sex differences in sociodemographic and clinical characteristics. It is possible that the clinicians perceive women's CVD risk as being lower than that of men, despite their history of a prior CVD event. Sex disparities in the levels and treatment of modifiable CVD risk factors may also reflect patient differences in knowledge and risk perception. In a large survey conducted in 2003 (18), 46% of the women were aware that heart disease is the leading killer of women, although only 31% cited high cholesterol and only 19% reported hypertension as causes of CVD. In another study of individuals who had experienced similar types of acute coronary syndrome events (19), women perceived their cardiac disease as less severe than did men. In addition, women with diabetes and their health care providers may not discuss cardiovascular risk and may place a higher priority on treating hyperglycemia and diabetes-related symptoms, as reflected by the observed lack of sex differences in A1C levels or management.

Our findings are consistent with previous reports concerning lipid control and management. We have reported previously (7) that women with diabetes in the TRIAD sample were significantly less likely than men with diabetes to receive lipid testing and lipid-lowering medications when all the patients, regardless of lipid levels, were considered. Others have reported similar results (8,9). Another study (20) has reported that 85% of the men and 82% of the women (P = 0.08) had either LDL cholesterol <3.35 mmol/l or received appropriate management (lipid medication initiation and intensification) if LDL cholesterol levels were ≥3.35 mmol/l. Because it has been reported that the use of a more detailed lipid-quality measure reduces the number of patients who appear to be receiving “suboptimal” care (16), we evaluated medication management of LDL cholesterol among the patients whose values were not in control. This measure gives credit for greater effort to manage this CVD risk factor and may reduce the effect of possible biological differences on quality assessment, such as diabetes having a greater adverse effect on lipids in women than in men (5). Nevertheless, we observed an ∼9% difference between men and women in the estimated probability of being treated with lipid-lowering medications.

The possibility of sex differences in management of blood pressure among patients with diabetes has received less attention. A study reported that among patients with a history of CVD who were treated with antihypertensive agents, the proportion of the patients with blood pressure levels ≥140/90 mmHg was somewhat higher in women than in men (unadjusted proportions 34 vs. 29%, P = 0.25) (9).

This study has several limitations. Given the limited time during which values of CVD risk factors and medications were abstracted, we used number of medication classes from the medical record at the time of risk factor measurements as a surrogate for intensity of care. We were not able to determine more doses or whether providers changed therapy in response to suboptimal control of a CVD risk factor. Second, medical records could not be obtained for ∼30% of the TRIAD sample because some patients did not provide consent. It is possible that quality of care might have been different for these patients. However, survey data indicated that patients with missing medical records were quite similar to the other participants in terms of sex distribution, sociodemographic characteristics, duration of diabetes, and self-reported health status (14). The strengths of this study include the large and demographically diverse sample of men and women with diabetes from several health plans and provider groups across the U.S., the objective measures of CVD risk factor levels and medication prescribed, and the availability of several sociodemographic and clinical characteristics.

It has been reported that an 8% improvement in cholesterol control for cardiac patients translates to an additional 7,200 people having their cholesterol effectively controlled and an estimated 250 lives saved (21). The unadjusted risk difference between men and women with history of CVD in the estimated probabilities of having LDL cholesterol in poor control reported here was 7.3%. These sex disparities might help to explain the sex disparities in CVD mortality observed in women with diabetes in a national sample (1,2). Given the proven effectiveness and cost-effectiveness of intensified lipid and blood pressure control in patients with diabetes in reducing CVD events and deaths (22–25), more intense treatment in women with diabetes offers the opportunity to reduce the observed gap between men and women with diabetes in the reduction of CVD mortality.

APPENDIX

Translating Research Into Action for Diabetes (TRIAD) Study Group

Hawaii Translational Research Center and Pacific Health Research Institute. Principal Investigator: J. David Curb, MD, MPH. Co-Principal Investigator: Beth Waitzfelder, PhD. Co-Investigators: Richard Chung, MD; R. Adams Dudley, MD, MBA; Chien-Wen Tseng, MD, MPH; Thomas Vogt, MD, MPH. Analyst: Qimei He, PhD. Project Coordinator: Suzanne Firrell. Indiana University Translational Research Center. Principal Investigator: David G. Marrero, PhD. Co-Principal Investigator: Ronald T. Ackermann, MD, MPH. Co-Investigators: Matthew J. Bair, MD, MS; Ed Brizendine, MS; Aaron E. Carroll, MD; Gilbert C. Liu, MD, MS; Paris Roach, MD; Changyu Shen, PhD; Morris Weinberger, PhD; Madelyn L. Wheeler, MS, RD, CD, FADA, CDE. Project Coordinator: Susanna R. Williams, MSPH. Division of Research, Kaiser Permanente Northern CA. Principal Investigator and Study Chairman: Joe V. Selby, MD, MPH. Co-Principal Investigator: Andrew J. Karter, PhD. Co-Investigators: Assiamira Ferrara, MD, PhD; Julie A. Schmittdiel, PhD. Senior Analysts: Connie Uratsu, Tiffany Peng. Project Coordinator: Bix E. Swain, MS. University of California, Los Angeles. Principal Investigator: Carol M. Mangione, MD, MSPH. Co-Principal Investigator: Arleen F. Brown, MD, PhD. Co-Investigators: O. Kenrik Duru, MD; Susan Ettner, PhD; Shaista Malik, MD, PhD; Martin F. Shapiro, MD, PhD. Senior Analyst: Neil Steers, PhD. Project Director: Elaine Quiter, RDA. Senior Administrator: Carole Nagy, BA. University of Medicine and Dentistry of New Jersey. Principal Investigator: Norman Lasser, MD, PhD. Co-Principal Investigator: Stephen H. Schneider, MD. Co-Investigator and Project Director: Dorothy A. Caputo, MA, APRN, BC-ADM, CDE. Co-Investigators: Jesse C. Crosson, PhD; Stephen Crystal, PhD; Monica Girotra, MD, Leslie-Faith Morritt Taub, DNSc, ANP-C, GNP-BC. Senior Analyst: Shou-En Lu, PhD. Analyst: Pin-Wen Wang, MPH. Central Activities Coordinating Center. TRIAD Central Program Assistant Director: Gabrielle J. Davis, BS, CHES. Central Program Development Specialist: Lucyna Lis, MA. Program Coordinator: Sonja Ross, BS, MHS. Senior Program Manager: William Marrone, EdS, EdM. University of Michigan Health System. Principal Investigator: William H. Herman, MD, MPH. Co-Investigators: Catherine Kim, MD, MPH; Michele Heisler, MD; Susan Johnson, MD; Kingsley Onyemere, MD. Research Associates: Ray Burke, MA; Laura McEwen, PhD, MPH; Bahman Tabaei, MPH. Project Director: Jennifer Goewey, MHA. Department of Veterans Affairs. Principal Investigator: Eve A. Kerr, MD, MPH. Co-Principal Investigator: Rodney A. Hayward, MD. Co-Investigators: Sarah Krein, PhD; John Piette, PhD. Project Managers: Mary Hogan, PhD, RN; Fatima Makki, MPH, MSW. Data Manager: Jennifer Davis, MPH. National Institute of Diabetes and Digestive and Kidney Diseases. Co-Investigator: Sanford A. Garfield, PhD. Centers for Disease Control and Prevention. Lead Consultant: K.M. Venkat Narayan, MD, MSc, MBA, MRCP. Collaborating Investigators: Gloria Beckles, MD, MSc; Patrick Boyle, PhD; Michael Engelgau, MD, MS; Tiffany Gary, PhD; Linda Geiss, MS; Robert Gerzoff, MS; Edward W. Gregg, PhD; Betsy L. (Cadwell) Gunnels; Roberta H. Hilsdon, BBA, AAS; Henry Kahn, MD, FACP; Jinan Saaddine, MD; Mark Stevens, MSPH, MA; Theodore Thompson, MS; Ed Tierney, MPH; Rodolfo Valdez, PhD; David F. Williamson, PhD; Ping Zhang, PhD. Management and Program Analyst: Shay Clayton. Preventive Effectiveness Fellow/Health Service Researcher: Rui Li, PhD. Program Administrator: Bernice Moore, MBA. Central Administrative Data Coordinator: Dori Bilik, MBA.