J. Michael McWilliams, MD, PhD; Ellen Meara, PhD; Alan M. Zaslavsky, PhD; John Z. Ayanian, MD, MPP
McWilliams JM, Meara E, Zaslavsky AM, Ayanian JZ. Differences in Control of Cardiovascular Disease and Diabetes by Race, Ethnicity, and Education: U.S. Trends From 1999 to 2006 and Effects of Medicare Coverage. Ann Intern Med. 2009;150:505-515. doi: 10.7326/0003-4819-150-8-200904210-00005
Download citation file:
Published: Ann Intern Med. 2009;150(8):505-515.
Efforts to improve the care of cardiovascular disease and diabetes or expand insurance coverage for adults with these conditions may reduce differences in clinical outcomes.
To assess recent national trends in disease control, trends in sociodemographic differences in control, and changes in sociodemographic differences after age 65 years associated with near-universal Medicare coverage.
Observational and quasi-experimental analyses of repeated cross-sectional data.
National Health and Nutrition Examination Survey, 1999 to 2006.
Adults age 40 to 85 years with relevant clinical conditions.
Blood pressure control (<140/90 mm Hg) and mean systolic blood pressure among adults with hypertension (n = 4521); glycemic control (hemoglobin A1c levels <7.0%) and mean hemoglobin A1c levels among those with diabetes (n = 1733); and total cholesterol level control (<5.2 mmol/L [<200 mg/dL]) and mean total cholesterol levels among those with coronary heart disease, stroke, or diabetes (n = 2928). Temporal trends in these measures were compared by race, ethnicity, and education, and sociodemographic differences were compared above and below eligibility for Medicare at age 65 years.
Disease control improved significantly between 1999 and 2006 for all 6 measures (PÂ < 0.001). These trends did not differ by race or ethnicity or by education (PÂ â‰¥ 0.185 for groupâ€“time interactions), except that whiteâ€“Hispanic differences in glycemic control widened (P = 0.042). Blackâ€“white differences in systolic blood pressure were smaller among adults age 65 to 85 years than among adults age 40 to 64 years (reduction in difference, 4.2 mm Hg; P = 0.009). Blackâ€“white differences in hemoglobin A1c levels were also smaller after age 65 years (reduction in difference, 0.7%; P = 0.005), as were Hispanicâ€“white differences (reduction in difference, 0.7%; P = 0.007) and differences between less and more educated adults (reduction in difference, 0.5%; P = 0.033).
Data were cross-sectional, and estimates may have been biased by coincidental events at age 65 years, such as retirement, that may affect disease control.
Control of blood pressure and glucose and cholesterol levels has improved since 1999 for adults with cardiovascular disease and diabetes, but racial, ethnic, or socioeconomic differences have not narrowed significantly. Medicare coverage after age 65 years is associated with reductions in these differences.
The Commonwealth Fund.
Acquiring health insurance and getting better quality of care could reduce health care disparities. The relative importance of these 2 factors is unknown.
To measure changes in chronic disease control, the authors used blood pressure, hemoglobin A1c, and total cholesterol measurements that were obtained from participants in the 1999 to 2006 National Health and Nutrition Examination Survey. Disease control improved over 8 years, but gaps between white and nonwhite patients did not change. The gaps were smaller after age 65 years, when universal Medicare insurance begins.
Each annual National Health and Nutrition Examination Survey enrolled different persons.
Access to care through universal health insurance reduced disparities in chronic disease control; improved quality of care did not affect sociodemographic differences.
Rates are plotted by survey period with 95% CIs and fitted trends from linear models. Disease control improved significantly from 1999 to 2006 for all 6 measures (all P < 0.001). A. Age- and sex-adjusted rates of blood pressure control and mean systolic blood pressure among adults with hypertension. B. Age- and sex-adjusted rates of glycemic control and mean hemoglobin A1c levels among adults with diabetes. C. Age- and sex-adjusted rates of total cholesterol level control and mean total cholesterol levels among adults with coronary heart disease, stroke, or diabetes. To convert cholesterol values from mg/dL to mmol/L, multiply by 0.0259.
Rates are plotted by race and ethnicity and survey period among adults with hypertension (A and B) and diabetes (C and D) with 95% CIs and fitted trends from linear models. Differences between black or Hispanic and white adults did not significantly change from 1999 to 2006 for any of the 4 measures of disease control, except differences in rates of glycemic control between Hispanic and white adults widened (P = 0.042). A. Age- and sex-adjusted rates of blood pressure control. B. Age- and sex-adjusted mean systolic blood pressure. C. Age- and sex-adjusted rates of glycemic control. D. Age- and sex-adjusted mean hemoglobin A1c levels.
All differences are displayed by age group; bars represent SEs. Higher values indicate greater disparities. Age groups are collapsed to reduce random variation, but narrower 5-year age intervals are shown adjacent to age 65 years to demonstrate notch effects. Differences among adults age 65 to 85 years were significantly smaller than those among adults age 40 to 64 years for systolic blood pressure (P = 0.009 for black–white comparison) and hemoglobin A1c levels (P < 0.001 for nonwhite–white comparison; P = 0.033 for comparison by education) and tended to be smaller for total cholesterol levels (P = 0.087 for comparison by education). Changes after age 65 years in black–white and Hispanic–white differences in hemoglobin A1c levels were equivalent, were each statistically significant, and were therefore combined into nonwhite–white differences to summarize. A. Differences in mean systolic blood pressure between black and white adults with hypertension or obesity. B. Differences in hemoglobin A1c levels between nonwhite and white adults with diabetes diagnosed before age 65 years. C. Differences in hemoglobin A1c levels between less and more educated adults with diabetes diagnosed before age 65 years. D. Differences in total cholesterol levels between less and more educated adults with coronary heart disease, stroke, or diabetes diagnosed before age 65 years. To convert cholesterol values from mg/dL to mmol/L, multiply by 0.0259.
The In the Clinic® slide sets are owned and copyrighted by the American College of Physicians (ACP). All text, graphics, trademarks, and other intellectual property incorporated into the slide sets remain the sole and exclusive property of the ACP. The slide sets may be used only by the person who downloads or purchases them and only for the purpose of presenting them during not-for-profit educational activities. Users may incorporate the entire slide set or selected individual slides into their own teaching presentations but may not alter the content of the slides in any way or remove the ACP copyright notice. Users may make print copies for use as hand-outs for the audience the user is personally addressing but may not otherwise reproduce or distribute the slides by any means or media, including but not limited to sending them as e-mail attachments, posting them on Internet or Intranet sites, publishing them in meeting proceedings, or making them available for sale or distribution in any unauthorized form, without the express written permission of the ACP. Unauthorized use of the In the Clinic slide sets will constitute copyright infringement.
James P. Scanlan
James P. Scanlan, Attorney at Law
November 28, 2009
Article on disparities in control of cardiovascular disease and diabetes raises several measurement issues
McWilliams et al.(1) analyze changes in disparities in dichotomous and continuous measures of control of cardiovascular disease and diabetes from 1999 to 2006 and the effects of Medicare coverage on these disparities. Three aspect of their analysis warrant comment.
First, the authors analyze the size of disparities in control rates in terms of absolute differences between rates. The analysis is flawed for failure to consider the way absolute differences between outcome rates tend to change solely because of changes in the overall prevalence of the outcome -- as I have discussed with respect to similar flaws in the references nos. 8, 11, and 20-22 of the article ([2-7). See also Houweling et al. (8), which similarly recognizes the patterns of correlations between absolute differences and the overall prevalence of an outcome and the need to consider overall prevalence in appraising the meaning of standard measures of differences between outcome rates. For research of this nature to be of value, it is necessary to appraise disparities between outcome rates in terms of measures that are unaffected by the overall prevalence of the outcome, such as that described in references 9 and 10, which derives differences between means of underlying risk distributions based on outcome rates (9,10).
But, in any case, it is not useful to analyze disparities in terms of absolute difference or other measures of differences between outcome rates that are affected by the prevalence of the outcome without even addressing the measurement issues. Notably, in observing that they are building on further work that found persistent or widening healthcare disparities in recent years, the authors cite the National Healthcare Disparities Report. But the National Healthcare Disparities Report not only relies on relative differences between rates rather than absolute differences, but relies on whichever relative difference (in the favorable or the adverse outcome) is the larger. Thus, as explained in references 2, 6, and 7 (and their corrections), as well as 10-12, with regard to the appraisal of the directions of changes in the size of disparities over time, the approach of the National Healthcare Disparities Report tends toward reaching exactly the opposite conclusions of those reached by researchers who rely on absolute differences (allowing, of course, that meaningful changes may counter the patterns that are solely statistically driven)(10-12).
Second, the authors also analyze the size of disparities in terms of continuous measures, including differences between average systolic blood pressure levels and average hemoglobin A1c levels. Such an approach may well avoid the problems associated with the ways that absolute differences and other standard measures of differences between outcome rates tend to be affected by the overall prevalence of an outcome. Where available data permit, such an approach -- making use, as it does, of the actual distributions of risk factors rather than a derived difference between means of a hypothetical risk distribution -- may well be much superior to the approach discussed in references 9 and 10.
But the most useful way to measure differences between means is by deriving an effect size from the arithmetic difference between means divided by the standard deviation (either the pooled standard deviation or that of one group or the other). The authors instead simply discuss the changes in terms of changes in the arithmetic differences between means. The authors highlight what they term a "0.7%" reduction in the race or ethnic difference and a "0.5%" reduction in the education difference for subjects over 65 compared with those under 65. The former reduction was from an absolute difference between mean levels of 0.9 percentage points to one of 0.2 percentage points; the latter was from 0.6 percentage points to 0.1 percentage points. Thus, allowing that the standard deviation serving as the denominator in the effect size fractions may differ somewhat for the 45 to 64 group and the over-65 group (NHANES 2005 data, which happen to be at hand, show a standard deviation of 1.23 for the former group and 0.94 for the latter group), these figures suggest a very substantial reduction in the disparity. The magnitude of such reduction is hardly reflected in the "0.7%" and "0.5%" figures used by the authors, even if such figures reflect valid measures.
Third, while the authors generally rely on percentage point difference and percentage point changes in figures, they almost invariably describe an "x percentage point" change as an "x%" change. Whether or not the latter usage is even technically correct, most readers regard an "x%" change as a percentage change not a percentage point change. The authors themselves in endeavoring to be clear in their discussion of the implications of differential increases in insurance coverage refer to a "6.6 percentage point" difference in increases in coverage rates. The entire article would be much clearer if discussion of absolute differences between percents were invariably described as percentage point differences rather than percent differences (13).
1. McWilliams JM, Meara E., Zaslavsky AM, Ayanian JZ. Differences in control of cardiovascular disease and diabetes by race, ethnicity, and education, U.S. trends from 1999 to 2006 and effects of Medicare coverage. Ann Int Med. 2009;150:505-515.
2. Scanlan JP. Effects of choice measure on determination of whether health care disparities are increasing or decreasing. Journal Review May 1, 2007 (responding to Trivedi AN, Zaslavsky AM, Schneider EC, Ayanian JZ. Trends in the quality of care and racial disparities in Medicare managed care. N Engl J Med 2005;353:692-700, and two other articles in the same issue): http://journalreview.org/v2/articles/view/16107620.html
3. Scanlan JP. Understanding patterns of absolute differences in vaccination rates in different settings. Journal Review Apr. 22, 2008 (responding to Schneider EC, Cleary PD, Zaslavsky AM, Epstein AM. Racial disparity in influenza vaccination: Does managed care narrow the gap between blacks and whites? JAMA 2001;286:1455-1460): http://journalreview.org/v2/articles/view/11572737.html
4. Scanlan JP. First learn to measure healthcare disparities. Health Affairs Mar. 12, 2008 (responding to Casalino LP, Elster A, Eisenberg A, et al. Will pay-for-performance and quality reporting affect health care disparities? Health Affairs 2007;26(3):405-414):: http://content.healthaffairs.org/cgi/eletters/26/3/w405
5. Scanlan JP. Inclusion of healthcare disparities issues in pay-for -performance programs should await development of reliable means of measuring changes in disparities over time. Journal Review Feb. 16, 2008 (responding to Casalino LP, Elster A, Eisenberg A, et al. Will pay-for- performance and quality reporting affect health care disparities? Health Affairs 2007;26(3):405-414): http://journalreview.org/v2/articles/view/17426053.html
6. Scanlan JP. Understanding the ways improvements in quality affect different measures of disparities in healthcare outcomes regardless of meaningful changes in the relationships between two distributions of factors associated with the outcome. Journal Review Aug. 30, 2007 (responding to Sequist TD, Adams AS, Zhang F, Ross-Degnan D, Ayanian JZ. The effect of quality improvement on racial disparities in diabetes care. Arch Intern Med 2006;166:675-681): http://journalreview.org/v2/articles/view/16567608.html
7. Scanlan JP. Understanding patterns of correlations between plan quality and different measures of healthcare disparities. Journal Review Aug. 30, 2007 (responding to Trivedi AN, Zaslavsky AM, Schneider EC, Ayanian JZ. Relationship between quality of care and racial disparities in Medicare health plans. JAMA 2006;296:1998-2004):: http://journalreview.org/v2/articles/view/17062863.html
8. Houweling TAJ, Kunst AE, Huisman M, Mackenbach JP. Using relative and absolute measures for monitoring health inequalities: experiences from cross-national analyses on maternal and child health. International Journal for Equity in Health 2007;6:15: http://www.equityhealthj.com/content/6/1/15
9. Solutions sub-page of Measuring Health Disparities page of jpscanlan.com http://www.jpscanlan.com/measuringhealthdisp/solutions.html
10. Can We Actually Measure Health Disparities?, presented at the 7th International Conference on Health Policy Statistics, Philadelphia, PA, Jan. 17-18, 2008: Oral Presentation: http://www.jpscanlan.com/images/2008_ICHPS_Oral.pdf; PowerPoint Presentation: http://www.jpscanlan.com/images/2008_ICHPS.ppt
11. Measurement Problems in the National Healthcare Disparities Report, presented at American Public Health Association 135th Annual Meeting & Exposition, Washington, DC, Nov. 3-7, 2007: PowerPoint Presentation: http://www.jpscanlan.com/images/APHA_2007_Presentation.ppt; Oral Presentation: http://www.jpscanlan.com/images/ORAL_ANNOTATED.pdf; Addendum (March 11, 2008): http://www.jpscanlan.com/images/Addendum.pdf
12. Scanlan JP. Study illustrates ways in which the direction of a change in disparity turns on the measure chosen. Pediatrics Mar. 27, 2008 (responding to Morita JY, Ramirez E, Trick WE. Effect of school-entry vaccination requirements on racial and ethnic disparities in Hepatitis B immunization coverage among public high school students. Pediatrics 2008;121:e547-e552): http://pediatrics.aappublications.org/cgi/eletters/121/3/e547
13. Percentage Points sub-page of Vignettes page of jpscanlan.com: http://www.jpscanlan.com/vignettes/percentgepoints.html
Cardiology, Endocrine and Metabolism, Diabetes, Healthcare Delivery and Policy, Coronary Risk Factors.
Results provided by:
Copyright © 2016 American College of Physicians. All Rights Reserved.
Print ISSN: 0003-4819 | Online ISSN: 1539-3704
Conditions of Use
This PDF is available to Subscribers Only