National Healthcare Disparities Report - LDI Health Economist

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Introduction and Methods antibiotic dose received within 6 hours of hospital arrival, influenza or pneumonia screening or vaccination) is divided by the sum of all of the opportunities to receive appropriate care. The CAHPS® (Consumer Assessment of Healthcare Providers and Systems) surveys have their own method for computing composite measures that has been in use for many years. These composite measures average individual components of patient experiences of care and are presented as the proportion of respondents who indicate that providers and/or systems sometimes or never, usually, or always performed well. Chapter 1 Two composite measures pertaining to patient safety are postoperative complications and complications from central venous catheters. For these composites, an additive model is used that sums individual complication rates. Thus, the numerator is the sum of individual complications and the denominator is the number of patients at risk for these complications. The composite rates are presented as the overall rate of complications. The postoperative complications composite is a good example of this type of composite measure: if 100 patients had a total of 30 complications among them (regardless of their distribution), the composite score would be 30%. On occasion, changes to the specification of a composite measure are made to better reflect clinical guidelines or to replace one of the measures of the composite that has improved beyond the 95% threshold. For the 2011 reports, the following changes to the specification of selected composite measures were made: n Heart failure treatment: Assessment of left ventricular ejection fraction replaced with use of ACE inhibitor. n Diabetes: Annual receipt of flu shot added and receipt of HbA1c changed from once to twice a year. Each year AHRQ staff, in conjunction with the Interagency Work Group, select a theme that will be explored in greater detail in the Highlights section, as well as in the body of the report. For 2011, the focus of the NHQR and NHDR is on understanding the quality of care rendered to America’s older population and the extent to which improvements in quality have occurred over time. Analyses In the NHQR, measures are tracked for different groups, such as age, gender, and geographic location. In the NHDR, comparisons are made across groups defined by race, ethnicity, income, education, activity limitations, and geographic location. In general, either the largest subgroup or the best performing subgroup is used as the reference group. Unless specified, the reference group is individuals ages 18-44 for age comparisons, individuals with private health insurance for insurance comparisons, and non-Hispanic Whites for racial and ethnic comparisons. Size of Disparities Across Groups Two criteria are applied to determine whether the difference between two groups is meaningful: n First, the difference between the two groups must be statistically significant with p
Introduction and Methods Adjusted percentages, which quantify the magnitude of disparities after controlling for a number of confounding factors, were generated for several measures in the Priority Populations chapter of the NHDR. In examining the relationship between race and ethnicity, for example, multivariate regression analyses were performed to control for differences in the distributions of income, education, insurance, age, gender, and geographic location. Trend Analyses In prior reports, a log-linear regression analysis was conducted to estimate average annual rate of change. v Historically, progress on individual measures was reported based solely on the magnitude of the annual rate of change. Progress on a measure was deemed to be improving if the annual rate of change was 1% or greater in the desirable direction. Progress on a measure was deemed to be getting worse when the annual rate of change was 1% or greater in the undesirable direction. Chapter 1 This approach is limited by the fact that, depending on the type of measure and the size of the standard error, a 1% difference may not be particularly meaningful. For instance, measures generated from administrative records (such as discharge data), which tend to have thousands or even millions of records, usually have smaller variances than other types of measures, such as those from surveys. The traditional approach for determining whether progress on a measure has been made does not consider the magnitude of error around an estimate, and no mechanism to ascertain whether such a change could have occurred by chance is used in making determinations about progress. It is therefore possible that, while a measure may meet the 1% threshold, annual rates of change may not be significant. Data used for trending are aggregate or average estimates for a measure, with data collected for a minimum of four data points (years), covering periods between 2000 and 2010. As such, trend analyses are generally conducted with a small number of observations. The level of precision across these points may be nonconstant, or heteroskedastic. Ideally, values with lower variances, indicative of greater precision, would be weighted more heavily than estimates with higher variances, or lower precision. With guidance from the Interagency Work Group methods subgroup, we identified and tested options for strengthening trend analyses by addressing heteroskedasticity or the amount of uncertainty around an estimate. A weighted log-linear model, where data points with lower variances are weighted more heavily than those with greater variances, as indicated below, was found to improve model fit. Model: Weight: ln(M) = 0 + 1 Y, where ln(M) is the natural logarithm of the value of the measure and 1 is the coefficient corresponding to year Y w = (M 2 /v), where M 2 /v is the square of the measure value and v is the variance v Regression models were specified as follows: ln(M) = 0 + 1 (Y), where ln(M) = natural logarithm of the measure value (M); 0 = intercept or constant; 1 (Y) = coefficient corresponding to year (Y). The average annual rate of change was calculated as 100 × (exp( 1 ) − 1). National Healthcare Disparities Report, 2011 43
Page 1: NATIONAL HEALTHCARE DISPARITIES REP
Page 4 and 5: Acknowledgments The NHQR and NHDR a
Page 6 and 7: National Healthcare Disparities Rep
Page 8 and 9: Highlights New this year, the Highl
Page 10 and 11: Highlights n Disparities in quality
Page 12 and 13: Highlights n Quality is improving s
Page 14 and 15: Highlights Figure H.6. Number and p
Page 16 and 17: Highlights Table H.2. Quality measu
Page 18 and 19: Highlights n Disparities also chang
Page 20 and 21: Highlights Figure H.9. Income-relat
Page 22 and 23: Highlights Progress in Patient Safe
Page 24 and 25: Highlights available to the public
Page 26 and 27: Highlights Examples of Initiatives
Page 28 and 29: Highlights Provider: When referring
Page 30 and 31: Highlights State: The Ohio Plan To
Page 32 and 33: Highlights Figure H.15. Number and
Page 34 and 35: Highlights to have high health care
Page 36 and 37: Highlights n Data on disparities ar
Page 38 and 39: Highlights share experiences and di
Page 40 and 41: Highlights References Andrulis DP,
Page 42 and 43: Introduction and Methods With rapid
Page 44 and 45: Introduction and Methods a new sect
Page 46 and 47: Introduction and Methods Table 1.2.
Page 50 and 51: Introduction and Methods Progress o
Page 52 and 53: Introduction and Methods n Trends o
Page 54 and 55: Introduction and Methods Reporting
Page 56 and 57: Effectiveness of Care Cancer Import
Page 58 and 59: Effectiveness of Care Chapter 2 Can
Page 60 and 61: Effectiveness of Care Treatment: Re
Page 62 and 63: Effectiveness of Care Also, in the
Page 64 and 65: Effectiveness of Care Importance Ca
Page 68 and 69: Effectiveness of Care Chapter 2 Car
Page 72 and 73: Effectiveness of Care Chronic Kidne
Page 74 and 75: Effectiveness of Care Figure 2.13.
Page 76 and 77: Effectiveness of Care Diabetes Impo
Page 78 and 79: Effectiveness of Care Multivariate
Page 80 and 81: Effectiveness of Care Focus on Indi
Page 82 and 83: Effectiveness of Care HIV and AIDS
Page 84 and 85: Effectiveness of Care To normalize
Page 86 and 87: Effectiveness of Care HIV/AIDS core
Page 88 and 89: Effectiveness of Care Outcome: Deat
Page 90 and 91: Effectiveness of Care Findings NEW
Page 96 and 97: Effectiveness of Care Prevention: W
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Effectiveness of Care Mental Health
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Effectiveness of Care Figure 2.31.
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Effectiveness of Care NEW Musculosk
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Effectiveness of Care n In all year
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Effectiveness of Care NEW Managemen
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Effectiveness of Care Findings Prev
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Effectiveness of Care Also, in the
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Effectiveness of Care Chapter 2 Res
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Effectiveness of Care Lifestyle Mod
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Effectiveness of Care n In 5 out of
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Effectiveness of Care NEW Outcome:
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Effectiveness of Care Chapter 2 Lif
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Effectiveness of Care Functional St
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Effectiveness of Care Chapter 2 Fun
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Effectiveness of Care at admission
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Effectiveness of Care Supportive an
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Effectiveness of Care n The 2008 to
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Effectiveness of Care Chapter 2 Sup
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Effectiveness of Care References 20
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Effectiveness of Care Centers for M
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Effectiveness of Care Oral health i
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140 National Healthcare Disparities
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Patient Safety To Err Is Human does
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Patient Safety Figure 3.1. Adult su
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Patient Safety Also, in the NHQR: n
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Patient Safety n From 2006-2007 to
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Patient Safety n From 2004 to 2008,
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Patient Safety Chapter 3 n From 200
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Timeliness Findings Getting Care fo
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Timeliness Chapter 4 n Not all pati
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Timeliness Chapter 4 n Among heart
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Patient Centeredness Chapter 5 Simi
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Patient Centeredness Figure 5.1. Ad
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Patient Centeredness Figure 5.3. Ch
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Patient Centeredness Figure 5.5. Ad
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Patient Centeredness Patient Langua
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Patient Centeredness Information ab
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Patient Centeredness Providers Aski
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178 National Healthcare Quality Rep
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Care Coordination Importance Chapte
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Care Coordination n In 2009, Americ
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Care Coordination Figure 6.3. Paren
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Care Coordination Also, in the NHQR
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Care Coordination Chapter 6 n Overa
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Care Coordination Chapter 6 n Overa
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Efficiency Findings Chapter 7 Inapp
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Efficiency Figure 7.2. Potentially
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Efficiency Comparisons with the 3 S
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Efficiency Perforated Appendixes Pe
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Efficiency Figure 7.8. Males age 75
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Health System Infrastructure Eviden
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Health System Infrastructure Figure
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Health System Infrastructure Figure
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Health System Infrastructure Occupa
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Health System Infrastructure n From
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Health System Infrastructure NEW Co
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Health System Infrastructure Table
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Health System Infrastructure Refere
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Access to Health Care Findings Heal
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Access to Health Care Figure 9.2. P
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Access to Health Care Financial Bur
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Access to Health Care Chapter 9 n O
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Priority Populations Chapter 10 232
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Priority Populations AHRQ’s Prior
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Priority Populations Blacks or Afri
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Priority Populations Hispanics or L
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Priority Populations Geographic are
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Priority Populations criminal justi
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Priority Populations Postponement R
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Priority Populations References Adl
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U.S. Department of Health and Human
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