Raynard Washington

Prevalence of Total Hip and Knee Replacement in the United States

BACKGROUND Descriptive epidemiology of total joint replacement procedures is limited to annual procedure volumes (incidence). The prevalence of the growing number of individuals living with a total hip or total knee replacement is currently unknown. Our objective was to estimate the prevalence of total hip and total knee replacement in the United States. METHODS Prevalence was estimated using the counting method by combining historical incidence data from the National Hospital Discharge Survey and the Healthcare Cost and Utilization Project (HCUP) State Inpatient Databases from 1969 to 2010 with general population census and mortality counts. We accounted for relative differences in mortality rates between those who have had total hip or knee replacement and the general population. RESULTS The 2010 prevalence of total hip and total knee replacement in the total U.S. population was 0.83% and 1.52%, respectively. Prevalence was higher among women than among men and increased with age, reaching 5.26% for total hip replacement and 10.38% for total knee replacement at eighty years. These estimates corresponded to 2.5 million individuals (1.4 million women and 1.1 million men) with total hip replacement and 4.7 million individuals (3.0 million women and 1.7 million men) with total knee replacement in 2010. Secular trends indicated a substantial rise in prevalence over time and a shift to younger ages. CONCLUSIONS Around 7 million Americans are living with a hip or knee replacement, and consequently, in most cases, are mobile, despite advanced arthritis. These numbers underscore the substantial public health impact of total hip and knee arthroplasties.

Identifying Increased Risk of Readmission and In-hospital Mortality Using Hospital Administrative Data: The AHRQ Elixhauser Comorbidity Index

Objective: We extend the literature on comorbidity measurement by developing 2 indices, based on the Elixhauser Comorbidity measures, designed to predict 2 frequently reported health outcomes: in-hospital mortality and 30-day readmission in administrative data. The Elixhauser measures are commonly used in research as an adjustment factor to control for severity of illness. Data Sources: We used a large analysis file built from all-payer hospital administrative data in the Healthcare Cost and Utilization Project State Inpatient Databases from 18 states in 2011 and 2012. Methods: The final models were derived with bootstrapped replications of backward stepwise logistic regressions on each outcome. Odds ratios and index weights were generated for each Elixhauser comorbidity to create a single index score per record for mortality and readmissions. Model validation was conducted with c-statistics. Results: Our index scores performed as well as using all 29 Elixhauser comorbidity variables separately. The c-statistic for our index scores without inclusion of other covariates was 0.777 (95% confidence interval, 0.776–0.778) for the mortality index and 0.634 (95% confidence interval, 0.633–0.634) for the readmissions index. The indices were stable across multiple subsamples defined by demographic characteristics or clinical condition. The addition of other commonly used covariates (age, sex, expected payer) improved discrimination modestly. Conclusions: These indices are effective methods to incorporate the influence of comorbid conditions in models designed to assess the risk of in-hospital mortality and readmission using administrative data with limited clinical information, especially when small samples sizes are an issue.

Google Flu Trends Spatial Variability Validated Against Emergency Department Influenza-Related Visits.

Background Influenza is a deadly and costly public health problem. Variations in its seasonal patterns cause dangerous surges in emergency department (ED) patient volume. Google Flu Trends (GFT) can provide faster influenza surveillance information than traditional CDC methods, potentially leading to improved public health preparedness. GFT has been found to correlate well with reported influenza and to improve influenza prediction models. However, previous validation studies have focused on isolated clinical locations. Objective The purpose of the study was to measure GFT surveillance effectiveness by correlating GFT with influenza-related ED visits in 19 US cities across seven influenza seasons, and to explore which city characteristics lead to better or worse GFT effectiveness. Methods Using Healthcare Cost and Utilization Project data, we collected weekly counts of ED visits for all patients with diagnosis (International Statistical Classification of Diseases 9) codes for influenza-related visits from 2005-2011 in 19 different US cities. We measured the correlation between weekly volume of GFT searches and influenza-related ED visits (ie, GFT ED surveillance effectiveness) per city. We evaluated the relationship between 15 publically available city indicators (11 sociodemographic, two health care utilization, and two climate) and GFT surveillance effectiveness using univariate linear regression. Results Correlation between city-level GFT and influenza-related ED visits had a median of .84, ranging from .67 to .93 across 19 cities. Temporal variability was observed, with median correlation ranging from .78 in 2009 to .94 in 2005. City indicators significantly associated (P<.10) with improved GFT surveillance include higher proportion of female population, higher proportion with Medicare coverage, higher ED visits per capita, and lower socioeconomic status. Conclusions GFT is strongly correlated with ED influenza-related visits at the city level, but unexplained variation over geographic location and time limits its utility as standalone surveillance. GFT is likely most useful as an early signal used in conjunction with other more comprehensive surveillance techniques. City indicators associated with improved GFT surveillance provide some insight into the variability of GFT effectiveness. For example, populations with lower socioeconomic status may have a greater tendency to initially turn to the Internet for health questions, thus leading to increased GFT effectiveness. GFT has the potential to provide valuable information to ED providers for patient care and to administrators for ED surge preparedness.

Impact of Race/Ethnicity and Socioeconomic Status on Risk-Adjusted Hospital Readmission Rates Following Hip and Knee Arthroplasty

BACKGROUND Readmission rates following total hip arthroplasty (THA) and total knee arthroplasty (TKA) are increasingly used to measure hospital performance. Readmission rates that are not adjusted for race/ethnicity and socioeconomic status, patient risk factors beyond a hospitals control, may not accurately reflect a hospitals performance. In this study, we examined the extent to which risk-adjusting for race/ethnicity and socioeconomic status affected hospital performance in terms of readmission rates following THA and TKA. METHODS We calculated 2 sets of risk-adjusted readmission rates by (1) using the Centers for Medicare & Medicaid Services standard risk-adjustment algorithm that incorporates patient age, sex, comorbidities, and hospital effects and (2) adding race/ethnicity and socioeconomic status to the model. Using data from the Healthcare Cost and Utilization Project, 2011 State Inpatient Databases, we compared the relative performances of 1,194 hospitals across the 2 methods. RESULTS Addition of race/ethnicity and socioeconomic status to the risk-adjustment algorithm resulted in (1) little or no change in the risk-adjusted readmission rates at nearly all hospitals; (2) no change in the designation of the readmission rate as better, worse, or not different from the population mean at >99% of the hospitals; and (3) no change in the excess readmission ratio at >97% of the hospitals. CONCLUSIONS Inclusion of race/ethnicity and socioeconomic status in the risk-adjustment algorithm led to a relative-performance change in readmission rates following THA and TKA at <3% of the hospitals. We believe that policymakers and payers should consider this result when deciding whether to include race/ethnicity and socioeconomic status in risk-adjusted THA and TKA readmission rates used for hospital accountability, payment, and public reporting. LEVEL OF EVIDENCE Prognostic Level III. See instructions for Authors for a complete description of levels of evidence.

Impact of Race/Ethnicity and Socioeconomic Status on Risk-Adjusted Readmission Rates Implications for the Hospital Readmissions Reduction Program

Under the Hospital Readmissions Reduction Program (HRRP) of the Centers for Medicare & Medicaid Services (CMS), hospitals with excess readmissions for select conditions and procedures are penalized. However, readmission rates are not risk adjusted for socioeconomic status (SES) or race/ethnicity. We examined how adding SES and race/ethnicity to the CMS risk-adjustment algorithm would affect hospitals’ excess readmission ratios and potential penalties under the HRRP. For each HRRP measure, we compared excess readmission ratios with and without SES and race/ethnicity included in the CMS standard risk-adjustment algorithm and estimated the resulting effects on overall penalties across a number of hospital characteristics. For the 5 HRRP measures (heart failure, acute myocardial infarction, chronic obstructive pulmonary disease, pneumonia, and total hip or knee arthroplasty), we used data from the Healthcare Cost and Utilization Project’s State Inpatient Databases for 2011-2012 to calculate the excess readmission ratio with and without SES and race/ethnicity included in the model. With these ratios, we estimated the impact on HRRP penalties and found that risk adjusting for SES and race/ethnicity would affect Medicare payments for 83.8% of hospitals. The effect on the size of HRRP penalties ranged from −14.4% to 25.6%, but the impact on overall Medicare base payments was small—ranging from −0.09% to 0.06%. Including SES and race/ethnicity in the calculation had a disproportionately favorable effect on safety-net and rural hospitals. Any financial effects on hospitals and on the Medicare program of adding SES and race/ethnicity to the HRRP risk-adjustment calculation likely would be small.

Differences in Use of High-quality and Low-quality Hospitals Among Working-age Individuals by Insurance Type.

Background: Research suggests that individuals with Medicaid or no insurance receive fewer evidence-based treatments and have worse outcomes than those with private insurance for a broad range of conditions. These differences may be due to patients’ receiving care in hospitals of different quality. Research Design: We used the Healthcare Cost and Utilization Project State Inpatient Databases 2009–2010 data to identify patients aged 18–64 years with private insurance, Medicaid, or no insurance who were hospitalized with acute myocardial infarction, heart failure, pneumonia, stroke, or gastrointestinal hemorrhage. Multinomial logit regressions estimated the probability of admissions to hospitals classified as high, medium, or low quality on the basis of risk-adjusted, in-hospital mortality. Results: Compared with patients who have private insurance, those with Medicaid or no insurance were more likely to be minorities and to reside in areas with low-socioeconomic status. The probability of admission to high-quality hospitals was similar for patients with Medicaid (23.3%) and private insurance (23.0%) but was significantly lower for patients without insurance (19.8%, P<0.01) compared with the other 2 insurance groups. Accounting for demographic, socioeconomic, and clinical characteristics did not influence the results. Conclusions: Previously noted disparities in hospital quality of care for Medicaid recipients are not explained by differences in the quality of hospitals they use. Patients without insurance have lower use of high-quality hospitals, a finding that needs exploration with data after 2013 in light of the Affordable Care Act, which is designed to improve access to medical care for patients without insurance.