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.

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 (O-3) and fine particulate matter with a diameter <= 2.5 mu m (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 <= 18 years with a principal diagnosis of pneumonia were included. Discharge data were linked to O-3 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) mu g/m(3). Median O-3 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 O-3 were associated with mortality, longer LOS, and greater costs. Greater O-3 levels were associated with increased odds of intubation. CONCLUSIONS: Greater levels of O-3 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.

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.

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.

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.

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.

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 fora 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.

Objectives: The aim of the Consensus on Health Economic Criteria (CHEC) project is to develop a criteria list for assessment of the methodological quality of economic evaluations in systematic reviews. The criteria list resulting from this CHEC project should be regarded as a minimum standard. Methods: The criteria list has been developed using a Delphi method. Three Delphi rounds were needed to reach consensus. Twenty-three international experts participated in the Delphi panel. Results: The Delphi panel achieved consensus over a generic core set of items for the quality assessment of economic evaluations. Each item of the CHEC-list was formulated as a question that can be answered by yes or no. To standardize the interpretation of the list and facilitate its use, the project team also provided an operationalization of the criteria list items. Conclusions: There was consensus among a group of international experts regarding a core set of items that can be used to assess the quality of economic evaluations in systematic reviews. Using this checklist will make future systematic reviews of economic evaluations more transparent, informative, and comparable. Consequently, researchers and policy-makers might use these systematic reviews more easily. The CHEC-list can be downloaded freely from http://www.beoz.unimaas.nl/chec/. manageably large number of trials and economic evaluations of health care interventions. Systematic reviews of these stud-ies can help in making well-informed decisions on which intervention to adopt. For maximum usefulness, systematic reviews of economic evaluations should be transparent, that is, all relevant methodological information from the included studies should be described in a systematic way. However, there is no generally accepted criteria list for reviewing eco-nomic evaluations, which may be because most of the criteria lists are created single-handed. The aim of the Consensus on

Mental and substance use disorders (M/SUDs) are major contributors to the global burden of disease, involving substantial social and economic costs.1 In the United States, an estimated 51.2 million adults aged 18 years or older (22.5 percent of adults) have experienced one or more M/SUDs in the past 12 months.2 Further, an estimated 8.4 million U.S. adults suffer from cooccurring M/SUDs—that is, they are affected by mental disorders (MDs) such as clinical depression or panic disorder, as well as by a substance use disorder (SUD) such as alcohol abuse or illicit drug dependence.3 Although many M/SUDs can be treated successfully in ambulatory care settings, inpatient treatment continues to be a key component of M/SUD care.

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.