Assessment of extreme heat and hospitalizations to inform early warning systems.

Heat early warning systems and action plans use temperature thresholds to trigger warnings and risk communication. In this study, we conduct multistate analyses, exploring associations between heat and all-cause and cause-specific hospitalizations, to inform the design and development of heat-health early warning systems. We used a two-stage analysis to estimate heat-health risk relationships between heat index and hospitalizations in 1,617 counties in the United States for 2003-2012. The first stage involved a county-level time series quasi-Poisson regression, using a distributed lag nonlinear model, to estimate heat-health associations. The second stage involved a multivariate random-effects meta-analysis to pool county-specific exposure-response associations across larger geographic scales, such as by state or climate region. Using results from this two-stage analysis, we identified heat index ranges that correspond with significant heat-attributable burden. We then compared those with the National Oceanic and Atmospheric Administration National Weather Service (NWS) heat alert criteria used during the same time period. Associations between heat index and cause-specific hospitalizations vary widely by geography and health outcome. Heat-attributable burden starts to occur at moderately hot heat index values, which in some regions are below the alert ranges used by the NWS during the study time period. Locally specific health evidence can beneficially inform and calibrate heat alert criteria. A synchronization of health findings with traditional weather forecasting efforts could be critical in the development of effective heat-health early warning systems.

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.

Trends in Opioid-related Inpatient Stays Shifted After the US Transitioned to ICD-10-CM Diagnosis Coding in 2015.

BACKGROUND: Trend analyses of opioid-related inpatient stays depend on the availability of comparable data over time. In October 2015, the US transitioned diagnosis coding from International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) to ICD-10-CM, increasing from ∼14,000 to 68,000 codes. This study examines how trend analyses of inpatient stays involving opioid diagnoses were affected by the transition to ICD-10-CM. SUBJECTS: Data are from Healthcare Cost and Utilization Project State Inpatient Databases for 14 states in 2015-2016, representing 26% of acute care inpatient discharges in the US. STUDY DESIGN: We examined changes in the number of opioid-related stays before, during, and after the transition to ICD-10-CM using quarterly ICD-9-CM data from 2015 and quarterly ICD-10-CM data from the fourth quarter of 2015 and the first 3 quarters of 2016. RESULTS: Overall, stays involving any opioid-related diagnosis increased by 14.1% during the ICD transition-which was preceded by a much lower 5.0% average quarterly increase before the transition and followed by a 3.5% average increase after the transition. In stratified analysis, stays involving adverse effects of opioids in therapeutic use showed the largest increase (63.2%) during the transition, whereas stays involving abuse and poisoning diagnoses decreased by 21.1% and 12.4%, respectively. CONCLUSIONS: The sharp increase in opioid-related stays overall during the transition to ICD-10-CM may indicate that the new classification system is capturing stays that were missed by ICD-9-CM data. Estimates of stays involving other diagnoses may also be affected, and analysts should assess potential discontinuities in trends across the ICD transition.

Association Between the Opening of Retail Clinics and Low-Acuity Emergency Department Visits.

STUDY OBJECTIVE: We assess whether the opening of retail clinics near emergency departments (ED) is associated with decreased ED utilization for low-acuity conditions. METHODS: We used data from the Healthcare Cost and Utilization Project State Emergency Department Databases for 2,053 EDs in 23 states from 2007 to 2012. We used Poisson regression models to examine the association between retail clinic penetration and the rate of ED visits for 11 low-acuity conditions. Retail clinic "penetration" was measured as the percentage of the ED catchment area that overlapped with the 10-minute drive radius of a retail clinic. Rate ratios were calculated for a 10-percentage-point increase in retail clinic penetration per quarter. During the course of a year, this represents the effect of an increase in retail clinic penetration rate from 0% to 40%, which was approximately the average penetration rate observed in 2012. RESULTS: Among all patients, retail clinic penetration was not associated with a reduced rate of low-acuity ED visits (rate ratio=0.999; 95% confidence interval=0.997 to 1.000). Among patients with private insurance, there was a slight decrease in low-acuity ED visits (rate ratio=0.997; 95% confidence interval=0.994 to 0.999). For the average ED in a given quarter, this would equal a 0.3% reduction (95% confidence interval 0.1% to 0.6%) in low-acuity ED visits among the privately insured if retail clinic penetration rate increased by 10 percentage points per quarter. CONCLUSION: With increased patient demand resulting from the expansion of health insurance coverage, retail clinics may emerge as an important care location, but to date, they have not been associated with a meaningful reduction in low-acuity ED visits.

Trends in prevalence of diabetes among delivery hospitalizations, United States, 1993-2009.

To describe recent trends in prevalence of pre-existing diabetes mellitus (PDM) (i.e., type 1 or type 2 diabetes) and gestational diabetes mellitus (GDM) among delivery hospitalizations in the United States. Data on delivery hospitalizations from 1993 through 2009 were obtained from the Health Care Cost and Utilization Project (HCUP) Nationwide Inpatient Sample. Diagnosis-Related Group codes were used to identify deliveries and diagnosis codes on presence of diabetes. Rates of hospitalizations with diabetes were calculated per 100 deliveries by type of diabetes, hospital geographic region, patient's age, degree of urbanicity of patient's residence, categorized median household income for patient's ZIP Code, expected primary payer, and type of delivery. From 1993 to 2009, age-standardized prevalence of diabetes per 100 deliveries increased from 0.62 to 0.90 for PDM (trend p < 0.001) and from 3.09 to 5.57 for GDM (trend p < 0.001). In 2009, correlates of PDM at delivery included older age [40-44 vs. 15-24: odds ratio 6.45 (95 % CI 5.27-7.88)], Medicaid/Medicare versus private payment sources [1.77 (95 % CI 1.59-1.98)], patient's ZIP Code with a median household income in bottom quartile versus other quartiles [1.54 (95 % CI 1.41, 1.69)], and C-section versus vaginal delivery [3.36 (95 % CI 3.10-3.64)]. Correlates of GDM at delivery were similar. Among U.S. delivery hospitalizations, the prevalence of diabetes is increasing. In 2009, the prevalence of diabetes was higher among women in older age groups, living in ZIP codes with lower household incomes, or with public insurance.

Hospital burden of unintentional carbon monoxide poisoning in the United States, 2007.

BACKGROUND: Unintentional, non-fire-related (UNFR) carbon monoxide (CO) poisoning is a leading cause of poisoning in the United States, but the overall hospital burden is unknown. This study presents patient characteristics and the most recent comprehensive national estimates of UNFR CO-related emergency department (ED) visits and hospitalizations. METHODS: Data from the 2007 Nationwide Inpatient and Emergency Department Sample of the Hospitalization Cost and Utilization Project were analyzed. The Council of State and Territorial Epidemiologists' CO poisoning case definition was used to classify confirmed, probable, and suspected cases. RESULTS: In 2007, more than 230,000 ED visits (772 visits/million) and more than 22,000 hospitalizations (75 stays/million) were related to UNFR CO poisoning. Of these, 21,304 ED visits (71 visits/million) and 2302 hospitalizations (8 stays/million) were confirmed cases of UNFR CO poisoning. Among the confirmed cases, the highest ED visit rates were among persons aged 0 to 17 years (76 visits/million) and 18 to 44 years (87 visits/million); the highest hospitalization rate was among persons aged 85 years or older (18 stays/million). Women visited EDs more frequently than men, but men were more likely to be hospitalized. Patients residing in a nonmetropolitan area and in the northeast and midwest regions of the country had higher ED visit and hospitalization rates. Carbon monoxide exposures occurred mostly (>60%) at home. The hospitalization cost for confirmed CO poisonings was more than $26 million. CONCLUSION: Unintentional, non-fire-related CO poisonings pose significant economic and health burden; continuous monitoring and surveillance of CO poisoning are needed to guide prevention efforts. Public health programs should emphasize CO alarm use at home as the main prevention strategy.

Sensitivity of household reported medical conditions in the medical expenditure panel survey.

INTRODUCTION: Accurate survey data on medical conditions are critical for health care researchers. Although medical condition data are complex and are subject to reporting error, little information exists on the quality of household reported condition data. METHODS: We used pooled data from 4 years (2002-2005) of the Medical Expenditure Panel Survey (MEPS) to estimate the extent to which household respondents may underreport 23 types of medical conditions. The medical expenditure panel survey is a nationally representative annual survey of approximately 15,000 households which collects medical condition information in 2 separate components-the Household Component (HC) and the Medical Provider Component (MPC). We computed sensitivity rates based on linked HC and MPC data under the assumption that if collection of medical conditions from household respondents was complete, then the conditions reported in the MPC would also be reported in the HC. RESULTS: Sensitivity rates ranged from a high of 93.8% to a low of 37.4% and were 75% or higher for 10 of the 23 conditions analyzed. The overall sensitivity rate for the 23 conditions combined was 74%. CONCLUSIONS: Household reports tended to be more accurate for conditions that are highly salient, cause pain, require hospitalization, require ongoing treatment, have specific recognizable treatment, alter lifestyle, and/or affect daily life (eg, pregnancy, diabetes, and kidney stones). In addition, reporting generally was better when conditions are classified in broader categories rather than in more detail.

Identification of hospital-acquired catheter-associated urinary tract infections from Medicare claims: sensitivity and positive predictive value.

BACKGROUND AND OBJECTIVE: Hospital-acquired catheter-associated urinary tract infection (CAUTI) is one of the first 6 conditions Medicare is targeting to reduce payment associated with hospital-acquired conditions under Congressional mandate. This study was to determine the positive predictive value (PPV) and sensitivity in identifying patients in Medicare claims who had urinary catheterization and who had hospital-acquired CAUTIs. RESEARCH DESIGN: CAUTIs identified by ICD-9-CM codes in Medicare claims were compared with those revealed by medical record abstraction in random samples of Medicare discharges in 2005 to 2006. Hospital discharge abstracts (2005) from the states of New York and California were used to estimate the potential impact of a present-on-admission (POA) indicator on PPV. RESULTS: ICD-9-CM procedure codes for urinary catheterization appeared in only 1.4% of Medicare claims for patients who had urinary catheters. As a proxy, claims with major surgery had a PPV of 75% and sensitivity of 48%, and claims with any surgical procedure had a PPV of 53% and sensitivity of 79% in identifying urinary catheterization. The PPV and sensitivity for identifying hospital-acquired CAUTIs varied, with the PPV at 30% and sensitivity at 65% in claims with major surgery. About 80% of the secondary diagnosis codes indicating UTIs were flagged as POA, suggesting that the addition of POA indicators in Medicare claims would increase PPV up to 86% and sensitivity up to 79% in identifying hospital-acquired CAUTIs. CONCLUSIONS: The validity in identifying urinary catheter use and CAUTIs from Medicare claims is limited, but will be increased substantially upon addition of a POA indicator.

Modifying ICD-9-CM coding of secondary diagnoses to improve risk-adjustment of inpatient mortality rates.

OBJECTIVE: To assess the effect on risk-adjustment of inpatient mortality rates of progressively enhancing administrative claims data with clinical data that are increasingly expensive to obtain. Data Sources. Claims and abstracted clinical data on patients hospitalized for 5 medical conditions and 3 surgical procedures at 188 Pennsylvania hospitals from July 2000 through June 2003. METHODS: Risk-adjustment models for inpatient mortality were derived using claims data with secondary diagnoses limited to conditions unlikely to be hospital-acquired complications. Models were enhanced with one or more of 1) secondary diagnoses inferred from clinical data to have been present-on-admission (POA), 2) secondary diagnoses not coded on claims but documented in medical records as POA, 3) numerical laboratory results from the first hospital day, and 4) all available clinical data from the first hospital day. Alternative models were compared using c-statistics, the magnitude of errors in prediction for individual cases, and the percentage of hospitals with aggregate errors in prediction exceeding specified thresholds. RESULTS: More complete coding of a few under-reported secondary diagnoses and adding numerical laboratory results to claims data substantially improved predictions of inpatient mortality. Little improvement resulted from increasing the maximum number of available secondary diagnoses or adding additional clinical data. CONCLUSIONS: Increasing the completeness and consistency of reporting a few secondary diagnosis codes for findings POA and merging claims data with numerical laboratory values improved risk adjustment of inpatient mortality rates. Expensive abstraction of additional clinical information from medical records resulted in little further improvement.

Association Between Outdoor Air Pollution Levels and Inpatient Outcomes in Pediatric Pneumonia Hospitalizations, 2007 to 2008.

OBJECTIVE: Pneumonia is a leading cause of pediatric admissions. Although air pollutants are associated with poor outcomes, few national studies have examined associations between pollutant levels and inpatient pediatric pneumonia outcomes. We examined the relationship between ozone (O3) and fine particulate matter with a diameter ≤2.5 µm (PM2.5) and outcomes related to disease severity. METHODS: In this cross-sectional study, we obtained discharge data from the 2007 to 2008 Nationwide Inpatient Sample and pollution data from the Air Quality System. Patients ≤18years with a principal diagnosis of pneumonia were included. Discharge data were linked to O3 and PM2.5 levels (predictors) from the patient's ZIP Code (not publicly available) from day of admission. Outcomes were mortality, intubation, length of stay (LOS), and total costs. We calculated weighted national estimates and performed multivariable analyses adjusting for sociodemographic and hospital factors. RESULTS: There were a total of 57,972 (278,871 weighted) subjects. Median PM2.5 level was 9.5 (interquartile range [IQR] 6.8-13.4) µg/m3. Median O3 level was 35.6 (IQR 28.2-45.2) parts per billion. Mortality was 0.1%; 0.75% of patients were intubated. Median LOS was 2 (IQR 2-4) days. Median costs were $3089 (IQR $2023-$5177). Greater levels of PM2.5 and O3 were associated with mortality, longer LOS, and greater costs. Greater O3 levels were associated with increased odds of intubation. CONCLUSIONS: Greater levels of O3 and PM2.5 were associated with more severe presentations of pneumonia. Future work should examine these relationships in more recent years and over a longer time period.

Using patient safety indicators to estimate the impact of potential adverse events on outcomes.

The authors estimated the impact of potentially preventable patient safety events, identified by Agency for Healthcare Research and Quality (AHRQ) Patient Safety Indicators (PSIs), on patient outcomes: mortality, length of stay (LOS), and cost. The PSIs were applied to all acute inpatient hospitalizations at Veterans Health Administration (VA) facilities in fiscal 2001. Two methods-regression analysis and multivariable case matching- were used independently to control for patient and facility characteristics while predicting the effect of the PSI on each outcome. The authors found statistically significant (p < .0001) excess mortality, LOS, and cost in all groups with PSIs. The magnitude of the excess varied considerably across the PSIs. These VA findings are similar to those from a previously published study of nonfederal hospitals, despite differences between VA and non-VA systems. This study contributes to the literature measuring outcomes of medical errors and provides evidence that AHRQ PSIs may be useful indicators for comparison across delivery systems.

How often are potential patient safety events present on admission?

BACKGROUND: Data fields that capture whether diagnoses are present on admission (POA)--distinguishing comorbidities from potential in-hospital complications--became part of the Uniform Bill for hospital claims in 2007. The AHRQ Patient Safety Indicators (PSIs) were initially developed as measures of potential patient safety problems that use routine administrative data without POA information. The impact of adding POA information to PSIs was examined. METHODS: Data were used from California (CA) and New York (NY) Healthcare Cost and Utilization Project (HCUP) state inpatient databases for 2003, which include POA codes. Analysis was limited to 13 of 20 PSIs for which POA information was relevant, such as complications of anesthesia, accidental puncture, and sepsis. RESULTS: In New York, 17% of cases revealed suspect POA coding, compared with 1%-2% in California. After suspect records were excluded, 92%-93% of secondary diagnoses in both CA and NY were POA. After incorporating POA information, most cases of decubitus ulcer (86%-89%), postoperative hip fracture (74%-79%), and postoperative pulmonary embolism/deep vein thrombosis (54%-58%) were no longer considered in-hospital patient safety events. DISCUSSION: Three of 13 PSIs appear not to be valid measures of in-hospital patient safety events, but the remaining 10 appear to be potentially useful measures even in the absence of POA codes.

Costly hospital readmissions and complex chronic illness.

People with multiple chronic conditions account for a large and disproportionate share of total health care costs. One aspect of the high cost for such patients is a relatively high number of hospital admissions per year. This study aims to clarify how the rate of hospital readmissions and hospital cost per person in a year depend on a patient's number of different chronic conditions ("complexity"), severity of illness, principal diagnosis at discharge, payer group, and other variables. We use a database of all hospital discharges for adults in six states. The number of different chronic conditions has a smoothly increasing effect on readmissions and cost per year, and there are notable differences by payer group. We offer illustrations of the potential savings from reducing total inpatient cost and readmissions in narrowly targeted populations with the most complex problems. The study's methods and descriptive data potentially could be useful for health plans and their sponsors (employers, government) when they design strategies to address the high cost of complex chronic illness.

Understanding and enhancing the value of hospital discharge data.

This work summarizes how hospital discharge data are used, identifies strengths and shortcomings, and presents suggestions for enhancing usefulness of the data. Results demonstrate that discharge data are used in a wide range of applications by diverse users. Uses include public health and population-based applications, as well as quality assessment, informed purchasing, strategic planning, and policy making. Strategies to enhance the utility of discharge data include: improving the quality of existing data elements and adding new data elements that will support more advanced analyses, improving linkages with data from nonhospital settings and databases outside health care, and developing a technical assistance network to support statewide data organizations in their efforts to collect and analyze discharge data. As our nation moves toward universal electronic medical records, it will be important to keep in mind the many uses of discharge data in order to maintain the data capacity to fill these needs.

Enhancement of claims data to improve risk adjustment of hospital mortality.

CONTEXT: Comparisons of risk-adjusted hospital performance often are important components of public reports, pay-for-performance programs, and quality improvement initiatives. Risk-adjustment equations used in these analyses must contain sufficient clinical detail to ensure accurate measurements of hospital quality. OBJECTIVE: To assess the effect on risk-adjusted hospital mortality rates of adding present on admission codes and numerical laboratory data to administrative claims data. DESIGN, SETTING, AND PATIENTS: Comparison of risk-adjustment equations for inpatient mortality from July 2000 through June 2003 derived by sequentially adding increasingly difficult-to-obtain clinical data to an administrative database of 188 Pennsylvania hospitals. Patients were hospitalized for acute myocardial infarction, congestive heart failure, cerebrovascular accident, gastrointestinal tract hemorrhage, or pneumonia or underwent an abdominal aortic aneurysm repair, coronary artery bypass graft surgery, or craniotomy. MAIN OUTCOME MEASURES: C statistics as a measure of the discriminatory power of alternative risk-adjustment models (administrative, present on admission, laboratory, and clinical for each of the 5 conditions and 3 procedures). RESULTS: The mean (SD) c statistic for the administrative model was 0.79 (0.02). Adding present on admission codes and numerical laboratory data collected at the time of admission resulted in substantially improved risk-adjustment equations (mean [SD] c statistic of 0.84 [0.01] and 0.86 [0.01], respectively). Modest additional improvements were obtained by adding more complex and expensive to collect clinical data such as vital signs, blood culture results, key clinical findings, and composite scores abstracted from patients' medical records (mean [SD] c statistic of 0.88 [0.01]). CONCLUSIONS: This study supports the value of adding present on admission codes and numerical laboratory values to administrative databases. Secondary abstraction of difficult-to-obtain key clinical findings adds little to the predictive power of risk-adjustment equations.

Identifying in-patient costs attributable to the clinical sequelae and comorbidities of alcoholic liver disease in a national hospital database.

BACKGROUND AND AIMS: The clinical sequelae and comorbidities of alcoholic liver disease (ALD) often require hospitalization. The aims of this study were to (1) compare the average costs of hospitalizations with ALD and the costs of hospitalizations with other alcohol-related diagnoses that do not involve the liver; and (2) estimate the percentage of the difference in costs between the ALD and non-ALD hospitalizations that may be attributed to ascites, protein-calorie malnutrition and other conditions. DESIGN: The 2012 National Inpatient Sample is a population-based cross-sectional database representing more than 94% of all discharges from community hospitals in the United States. SETTING: Community hospitals in the United States. PARTICIPANTS: The sample included 72 531 hospitalizations with ALD and 287 047 hospitalizations with other alcohol-related diagnoses. MEASUREMENTS: The dependent variable was total in-patient costs. We estimated the contribution of ascites, protein-calorie malnutrition and other conditions to the difference in costs between patients with ALD and patients with other diagnoses. FINDINGS: Average costs for ALD patients were $3188.4 higher than those for patients with other diagnoses ($13 543 versus $10 355; P < 0.001). Among all conditions in the analysis, protein-calorie malnutrition had the largest impact on costs [$6501; 95% confidence interval (CI) = 5956, 7045; P < 0.001] accounting for 12% of the higher costs of ALD stays. CONCLUSIONS: Costs of hospital care for patients with alcoholic liver disease are higher than those for patients with other alcohol-related diagnoses. These increased costs are associated with specific clinical sequelae and comorbidities, with protein-calorie malnutrition-a largely preventable condition-making a substantial contribution.

Hospital inpatient costs for adults with multiple chronic conditions.

This article offers national estimates of the proportions of hospital inpatient cases and cost for adult, nonmaternal patients who have multiple chronic conditions. The authors employ a refined classification of chronic versus acute conditions, collapsed to no more than one condition per distinct category of condition. The number of different chronic conditions provides a simple measure of complexity, differing from measures of severity of illness that pertain to a particular episode of treatment. A multivariate regression finds that the number of chronic conditions is an independent influence on hospital cost per case, controlling for other key determinants. Patients with complex illness (e.g., 3+ or 5+ chronic conditions) have a disproportionately large effect on hospital cost per year. The identification of patients in the hospital with complex illness can help in targeting new covered services in a health plan or in risk adjusting health plan premiums. Current policies and demonstrations for the Medicare program may not be sufficient to address complex illness.

Health care for children and youth in the United States: annual report on patterns of coverage, utilization, quality, and expenditures by income.

OBJECTIVES: To examine differences by income in insurance coverage, health care utilization, expenditures, and quality of care for children in the United States. METHODS: Two national health care databases serve as the sources of data for this report: the 2000-2002 Medical Expenditure Panel Survey (MEPS) and the 2001 Nationwide Inpatient Sample (NIS) from the Healthcare Cost and Utilization Project (HCUP). In the MEPS analyses, low income is defined as less than 200% of the federal poverty level and higher income is defined as 200% of the federal poverty level or more. For the HCUP analyses, median household income for the patient's zip code of residence is used to assign community-level income to individual hospitalizations. RESULTS: Coverage. Children from low-income families were more likely than children from middle-high-income families to be uninsured (13.0% vs 5.8%) or covered by public insurance (50.8% vs 7.3%), and less likely to be privately insured (36.2% vs 87.0%). Utilization. Children from low-income families were less likely to have had a medical office visit or a dental visit than children from middle-high-income families (63.7% vs 76.5% for office-based visits and 28.8% vs 51.4% for dental visits) and less likely to have medicines prescribed (45.1% vs 56.4%) or have utilized hospital outpatient services (5.2% vs 7.0%), but more likely to have made trips to the emergency department (14.6% vs 11.4%). Although low-income children comprise almost 40% of the child population, one quarter of total medical expenditures were for these children. Hospital Discharges. Significant differences by community-level income occurred in specific characteristics of hospitalizations, including admissions through the emergency department, expected payer, mean total charges per day, and reasons for hospital admission. Leading reasons for admission varied by income within and across age groups. Quality. Low-income children were more likely than middle-high-income children to have their parents report a big problem getting necessary care (2.4% vs 1.0%) and getting a referral to a specialist (11.5% vs 5.3%). Low-income children were at least twice as likely as middle-high-income children to have their parents report that health providers never/sometimes listened carefully to them (10.0% vs 5.1%), explained things clearly to the parents (9.6% vs 3.4%), and showed respect for what the parents had to say (9.2% vs 4.2%). Children from families with lower community-level incomes were more likely to experience ambulatory-sensitive hospitalizations. Racial/Ethnic Differences Between Income Groups. Use and expenditure patterns for most services were not significantly different between low- and middle-high-income black children and were lower than those for white children. CONCLUSIONS: While health insurance coverage is still an important factor in obtaining health care, the data suggest that efforts beyond coverage may be needed to improve access and quality for low-income children overall and for children who are racial and ethnic minorities, regardless of income.

Using the AHRQ quality indicators to improve health care quality.

In summary, the AHRQ QIs are a set of readily available programs that can be downloaded without charge from the AHRQ Web site. The methodology is completely open and accessible to all users. The QI software can be applied to hospital administrative data that is available within individual institutions or from state data organizations and hospital associations and can provide valuable insights into health care quality at extremely low cost. The QIs have been incorporated into numerous quality assessment reports, including hospital-specific reports, with the aim of improving health care quality at a reasonable cost. With enhancements currently underway, the QIs will be an even more valuable part of the toolkit to improve health care quality in the United States.