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Published online January 17, 2008
Diabetes Care 31:728-731, 2008
DOI: 10.2337/dc07-1431
© 2008 by the American Diabetes Association
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Epidemiology/Health Services Research
Original Research

Individualized, Non–Age-Based Glycemic Control in Elderly Veterans With Diabetes

Drew A. Helmer, MD1,2, Usha Sambamoorthi, PHD2,3, Mangala Rajan, MBA1, Chin-Lin Tseng, DPH1,2 and Leonard M. Pogach, MD1,2

1 Center for Healthcare Knowledge Management, Veterans Affairs New Jersey Health Care System, East Orange, New Jersey
2 New Jersey Medical School, University of Medicine and Dentistry New Jersey, Newark, New Jersey
3 Department of Community Health and Preventive Medicine, Morehouse School of Medicine, Atlanta, Georgia

Address correspondence and reprint requests to Drew Helmer, MD, MEDVAMC, 2002 Holcombe Blvd. (111PC), Houston, TX 77030. E-mail: drew.helmer{at}va.gov


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 RESEARCH DESIGN AND METHODS
 RESULTS--
 CONCLUSIONS--
 References
 
OBJECTIVE—To examine the role of age and endocrinology care in glycemic testing and control in elderly veterans with diabetes.

RESEARCH DESIGN AND METHODS—In this retrospective study of Veterans Health Administration clinic users aged ≥65 years with diabetes, we compared glycemic testing and poor glycemic control (A1C >9%) between older (≥75 years) and younger (65–74 years) veterans in the year 2000.

RESULTS—Without adjustment, rates for glycemic testing were 70.2% in older and 71.1% in younger veterans, and those for poor control were 9.4% in older and 12.8% in younger veterans. After adjustment, older veterans had 1.8% lower probability of glycemic testing and 2.9% lower probability of poor control than younger veterans. Endocrinology care was associated with a higher probability of both glycemic testing (9.7%) and poor control (1.0%), regardless of age.

CONCLUSIONS—Glycemic testing and control and effect of endocrinology care were comparable in older and younger veterans with diabetes.

Abbreviations: FY, fiscal year • RRS, relative risk score • VHA, Veterans Health Administration


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
RESEARCH DESIGN AND METHODS
 RESULTS--
 CONCLUSIONS--
 References
 
Diabetes affects 20% of individuals aged ≥65 years (1). While glycemic testing and control using A1C are indicated for all people with diabetes (2), glycemic management should be individually tailored, especially in the geriatric population (3). Using age rather than relevant clinical and functional considerations to guide management decisions may lead to a systematic bias (i.e., age disparity). Under optimal care, however, one should not detect differences in rates of glycemic testing and poor control due to age. This article compares glycemic testing and poor glycemic control (A1C >9%) rates between younger (aged 65–74 years) and older (aged ≥75 years) veterans.


   RESEARCH DESIGN AND METHODS
TOP
 ABSTRACT
 INTRODUCTION
 RESEARCH DESIGN AND METHODS
RESULTS--
 CONCLUSIONS--
 References
 
Data
We used Veterans Health Administration (VHA) and Medicare data from the Diabetes Epidemiology Cohort of diabetic individuals who used the VHA for health care (4). Our inclusion criteria were as follows: that subjects be aged ≥65 years, have had one or more VHA primary care visits in 1999, have continuous Medicare fee-for-service enrollment, and be alive on 30 September 2000. The Veterans Affairs New Jersey Health Care System approved the study.

Dependent variables
A1C testing in fiscal year (FY) 2000 was identified using Current Procedural Terminology codes in VHA or Medicare data (5). For individuals with A1C values available (Medicare data do not include laboratory values), we dichotomized individuals’ last FY 2000 A1C value to highlight poor glycemic control (A1C >9%). Despite controversy (6), comparisons based on poor glycemic control minimize the confounding problems of comorbidity and patient preferences. Experts agree that poor glycemic control should be addressed in all diabetic patients to minimize symptoms (7,8).

Key independent variable
Subjects were categorized as young-old (65–74 years) or old-old (≥75 years) in FY 1999, based on differences in life expectancies. Americans live an average of 18.4 years after reaching 65 years of age and 11.8 years after reaching 75 years of age (9).

Independent variables (from FY 1999)
Variables consisted of demographics (sex, race/ethnicity, and marital status), socioeconomic status (VHA priority group status), access to health care (Medicaid or Medicare Part B enrollment), endocrinology care, medical comorbidity, and mental illness based on ICD-9-Clinical Modification codes (10). Medical comorbidity was based on individuals’ DxCG relative risk score (RRS) using ICD-9 codes from VHA and Medicare inpatient and outpatient records. The RRS is normalized to a mean of 1.0 (range 0.8–146.0) (DxCG Risk Smart, Revision A; DxCG, Boston, MA). Endocrinologist care was determined from clinic stop codes in VHA data and physician specialty codes listed on Medicare claims.

Analytic procedures
We used probit regressions to examine the association between age and diabetes care after controlling for other independent variables. We calculated marginal effects by transforming parameter estimates to probabilities of an outcome. Marginal effect is interpreted as the change in probability of experiencing the dependent variable in response to a 1-unit change in the independent variable (e.g., from RRS [comorbidity score] of 1.0 to 2.0, and from reference group of white to comparison group of Latino).

We estimated the interaction effect of age and endocrinology care on both outcomes using the NLCOM procedure in Stata (version 8.2; Stata, College Station, TX) rather than basing statistical inferences on the coefficient and significance level of the interaction term produced by probit regressions (11). We considered associations statistically significant at P < 0.01.


   RESULTS—
TOP
 ABSTRACT
 INTRODUCTION
 RESEARCH DESIGN AND METHODS
 RESULTS--
CONCLUSIONS--
 References
 
The table describes the study population (n = 194,772) by age categories: age 65–75 years (n = 74,772 [38%]) and age ≥75 years (n = 120,000 [62%]). These groups were significantly different in all independent variables except Medicaid enrollment (P = 0.329) and marital status (P = 0.113). Overall, 85,288 of veterans aged 65–75 years (71.1%) and 52,487 (70.2%) of those aged ≥75 years underwent A1C testing (P < 0.001). Among those with an available A1C value (n = 112,168), fewer in the older age-group (9.4%; n = 3,860) compared with the younger age-group (12.8%; n = 9,125) had A1C >9% (P < 0.001).

Endocrinology care was associated with higher rates of glycemic testing (79.2 vs. 68.4% in those aged ≥75 years and 78.8 vs. 69.2% in those aged 65–75 years) and poor control (10.8 vs. 9.1% and 13.5 vs. 12.6%, respectively). Analysis of interaction terms determined (data not shown) that endocrinology care did not modify the associations between age and glycemic testing (P = 0.012) or age and poor glycemic control (P = 0.021). Therefore, we present the results for regression models without these interaction terms. After controlling for other variables, we determined that veterans aged ≥75 years had 1.8% lower probability of having A1C tested and 2.9% lower probability of poor glycemic control than those aged 65–75 years (P < 0.001) (Table 1). Overall, endocrinology care was associated with a higher probability of both glycemic testing (9.7%) and poor control (1.0%).


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  		Table 1— Study population characteristics by age and age disparities in glycemic testing and poor glycemic control among elderly veteran clinic users with diabetes

 

   CONCLUSIONS—
TOP
 ABSTRACT
 INTRODUCTION
 RESEARCH DESIGN AND METHODS
 RESULTS--
 CONCLUSIONS--
References
 
Elderly VHA users with diabetes aged ≥75 years had lower, but clinically insignificant, probability of receiving glycemic testing than those aged 65–75 years; they also had lower probability of poor glycemic control. Our data do not support the hypothesized age-based differential of glycemic management in the VHA, similar to the results for the general U.S. population (12).

Our analyses show that receipt of endocrinology care is similar regardless of age. Endocrinology care was associated with higher likelihood of A1C testing and poor glycemic control, likely reflecting a preference to refer patients with poor glycemic control to endocrinologists (13).

Other studies have also found that older individuals have better glycemic control than younger individuals (14,15). It is possible that differences in diabetes diagnosis due to more frequent blood testing among the elderly (16), diabetes pathophysiology, and/or a survival bias in older patients (15) may obscure an age disparity in glycemic management.

The rates of glycemic testing and poor control found in this analysis are consistent with other studies (17–19), demonstrating the need for improvement of care for elderly diabetic patients. The strengths of our study include the national scope of the population, examination of VHA and Medicare data, and well-established definitions of diabetes, comorbidities, glycemic testing, and poor glycemic control. Some data were missing for the few individuals who relied on Medicaid services. Lack of A1C values in Medicare claims data limited our glycemic control analysis to a subset of this population. We were unable to examine specific comorbidities that impact glycemic control such as vision loss and cognitive impairment. Finally, our definition of endocrinology care has face validity and is likely to be sensitive, but not specific, to diabetes care. Our findings must be generalized with care; the VHA serves a primarily older, white male population with an emphasis on geriatric care that may not be typical of other health care systems.

This study documents the lack of a meaningful age disparity in glycemic testing or control among elderly veterans with diabetes. It provides a baseline for surveillance for age disparities in this population and informs future research about glycemic management for elderly people with diabetes.


   Acknowledgments
 
Project support provided by Veterans Affairs Health Services research and development grants to Drs. D.A. Helmer (RCD-02-041-2), L.M. Pogach (IIR 02-079), and U. Sambamoorthi (IIR-05-016).


   Footnotes
 
Published ahead of print at http://care.diabetesjournals.org on 17 January 2008. DOI: 10.2337/dc07-1431.

The views expressed are the authors’ and do not necessarily reflect the position or policy of the Department of Veterans Affairs.

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked "advertisement" in accordance with 18 U.S.C Section 1734 solely to indicate this fact.

Received for publication July 24, 2007. Accepted for publication January 7, 2008.


   References
TOP
 ABSTRACT
 INTRODUCTION
 RESEARCH DESIGN AND METHODS
 RESULTS--
 CONCLUSIONS--
 References
 

  1. National Diabetes Fact Sheet: United States, 2003 [article online], 2003. Available from http://www.cdc.gov/DIABETES/pubs/pdf/ndfs2003.pdf. Accessed 26 May 2006
  2. American Diabetes Association: Standards of medical care in diabetes–2006 (Position Statement). Diabetes Care 29:S4–S42, 2006[Free Full Text]
  3. California Healthcare Foundation/American Geriatrics Society Panel on Improving Care for Elders with Diabetes: Guidelines for improving the care of the older person with diabetes mellitus. J Am Geriatr Soc 51:S265–S280, 2003[Medline]
  4. Miller DR, Safford M, Pogach L: Who has diabetes? Best estimates of diabetes prevalence in the Department of Veterans Affairs based on computerized patient data. Diabetes Care 27(Suppl. 2):B10–B21, 2004[Abstract/Free Full Text]
  5. National Committee for Quality Assurance (NCQA): HEDIS 2004: technical specifications. Washington, DC, National Committee for Quality Assurance, 2003
  6. Huang ES, Sachs GA, Chin MH: Implications of new geriatric diabetes care guidelines for the assessment of quality of care in older patients. Medical Care 44:373–377, 2006[Medline]
  7. Health Care Financing Administration: Diabetes Quality Improvement Project: Initial Measure Set [Final Version]. Washington, DC, 1998
  8. Fleming BF, Greenfield S, Engelgau MM, Pogach LM, Clauser SB, Parrott MA, the DQIP Group: The Diabetes Quality Improvement Project (DQIP): moving science into health policy to gain an edge on the diabetes epidemic. Diabetes Care 24:1815–1820, 2001[Free Full Text]
  9. Health, United States 2005 [article online], 2006. Available from http://www.cdc.gov/nchs/data/hus/hus05.pdf#027. Atlanta, GA, Centers for Disease Control. Accessed 15 February 2008
  10. Frayne SM, Halanych JH, Miller DR, Wang F, Lin H, Pogach L, Sharkansky EJ, Keane TM, Skinner KM, Rosen CS, Berlowitz DR: Disparities in diabetes care: impact of mental illness. Arch Intern Med 165:2631–2638, 2005[Abstract/Free Full Text]
  11. Norton EC, Wang H, Ai C: Computing interaction effects in logit and probit models. The Stata Journal 4:154–167, 2004
  12. Shorr RI, Franse LV, Resnick HE, Di BM, Johnson KC, Pahor M: Glycemic control of older adults with type 2 diabetes: findings from the Third National Health and Nutrition Examination Survey, 1988–1994. J Am Geriatr Soc 48:264–267, 2000[Medline]
  13. Nguyen CV, Powers AC, Greenspan DL, Elasy TA: Diabetes referrals at a Veterans Administration tertiary facility: who are the patients and why are they referred? Diabetes Care 28:423–424, 2005[Free Full Text]
  14. Katon W, Von KM, Ciechanowski P, Russo J, Lin E, Simon G, Ludman E, Walker E, Bush T, Young B: Behavioral and clinical factors associated with depression among individuals with diabetes. Diabetes Care 27:914–920, 2004[Abstract/Free Full Text]
  15. Bruce DG, Davis WA, Davis TM: Glycemic control in older subjects with type 2 diabetes mellitus in the Fremantle Diabetes Study. J Am Geriatr Soc 48:1449–1453, 2000[Medline]
  16. Edelman D: Rate and predictors of glycemic testing of nondiabetic patients in a managed care population. Diabetes Care 24:957–959, 2001[Free Full Text]
  17. Kerr EA, Gerzoff RB, Krein SL, Selby JV, Piette JD, Curb JD, Herman WH, Marrero DG, Narayan KM, Safford MM, Thompson T, Mangione CM: Diabetes care quality in the Veterans Affairs Health Care System and commercial managed care: the TRIAD study. Ann Intern Med 141:272–281, 2004[Abstract/Free Full Text]
  18. Asch SM, McGlynn EA, Hogan MM, Hayward RA, Shekelle P, Rubenstein L, Keesey J, Adams J, Kerr EA: Comparison of quality of care for patients in the Veterans Health Administration and patients in a national sample. Ann Intern Med 141:938–945, 2004[Abstract/Free Full Text]
  19. Jencks SF, Huff ED, Cuerdon T: Change in the quality of care delivered to Medicare beneficiaries, 1998–1999 to 2000–2001. JAMA 289:305–312, 2003[Abstract/Free Full Text]

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This Article
Right arrow Abstract Freely available
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