Hospital standardized mortality ratios: sensitivity analyses on the impact of coding.
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ABSTRACT: Introduction
Hospital standardized mortality ratios (HSMRs) are derived from administrative databases and cover 80 percent of in-hospital deaths with adjustment for available case mix variables. They have been criticized for being sensitive to issues such as clinical coding but on the basis of limited quantitative evidence.Methods
In a set of sensitivity analyses, we compared regular HSMRs with HSMRs resulting from a variety of changes, such as a patient-based measure, not adjusting for comorbidity, not adjusting for palliative care, excluding unplanned zero-day stays ending in live discharge, and using more or fewer diagnoses.Results
Overall, regular and variant HSMRs were highly correlated (?>0.8), but differences of up to 10 points were common. Two hospitals were particularly affected when palliative care was excluded from the risk models. Excluding unplanned stays ending in same-day live discharge had the least impact despite their high frequency. The largest impacts were seen when capturing postdischarge deaths and using just five high-mortality diagnosis groups.Conclusions
HSMRs in most hospitals changed by only small amounts from the various adjustment methods tried here, though small-to-medium changes were not uncommon. However, the position relative to funnel plot control limits could move in a significant minority even with modest changes in the HSMR.
SUBMITTER: Bottle A
PROVIDER: S-EPMC3393030 | biostudies-literature | 2011 Dec
REPOSITORIES: biostudies-literature
Publications
Hospital standardized mortality ratios: sensitivity analyses on the impact of coding.
Bottle Alex A Jarman Brian B Aylin Paul P
Health services research 20110725 6pt1
<h4>Introduction</h4>Hospital standardized mortality ratios (HSMRs) are derived from administrative databases and cover 80 percent of in-hospital deaths with adjustment for available case mix variables. They have been criticized for being sensitive to issues such as clinical coding but on the basis of limited quantitative evidence.<h4>Methods</h4>In a set of sensitivity analyses, we compared regular HSMRs with HSMRs resulting from a variety of changes, such as a patient-based measure, not adjust ...[more]
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Project description:BackgroundResults of associations between process and mortality indicators, both used for the external assessment of hospital care quality or public reporting, differ strongly across studies. However, most of those studies were conducted in North America or United Kingdom. Providing new evidence based on French data could fuel the international debate on quality of care indicators and help inform French policy-makers. The objective of our study was to explore whether optimal care delivery in French hospitals as assessed by their Hospital Process Indicators (HPIs) is associated with low Hospital Standardized Mortality Ratios (HSMRs).MethodsThe French National Authority for Health (HAS) routinely collects for each hospital located in France, a set of mandatory HPIs. Five HPIs were selected among the process indicators collected by the HAS in 2009. They were measured using random samples of 60 to 80 medical records from inpatients admitted between January 1st, 2009 and December 31, 2009 in respect with some selection criteria. HSMRs were estimated at 30, 60 and 90 days post-admission (dpa) using administrative health data extracted from the national health insurance information system (SNIIR-AM) which covers 77% of the French population. Associations between HPIs and HSMRs were assessed by Poisson regression models corrected for measurement errors with a simulation-extrapolation (SIMEX) method.ResultsMost associations studied were not statistically significant. Only two process indicators were found associated with HSMRs. Completeness and quality of anesthetic records was negatively associated with 30 dpa HSMR (0.72 [0.52-0.99]). Early detection of nutritional disorders was negatively associated with all HSMRs: 30 dpa HSMR (0.71 [0.54-0.95]), 60 dpa HSMR (0.51 [0.39-0.67]) and 90 dpa HSMR (0.52 [0.40-0.68]).ConclusionIn absence of gold standard of quality of care measurement, the limited number of associations suggested to drive in-depth improvements in order to better determine associations between process and mortality indicators. A smart utilization of both process and outcomes indicators is mandatory to capture aspects of the hospital quality of care complexity.
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Project description:BackgroundStandardized Mortality Ratios (SMRs) are case-mix adjusted mortality rates per hospital and are used to evaluate quality of care. However, acute care is increasingly organized on a regional level, with more severe patients admitted to specialized hospitals. We hypothesize that the current case-mix adjustment insufficiently captures differences in case-mix between non-specialized and specialized hospitals. We aim to improve the SMR by adding proxies of disease severity to the model and by calculating a regional SMR (RSMR) for acute cerebrovascular disease (CVD) and myocardial infarction (MI).MethodsWe used data from the Dutch National Basic Registration of Hospital Care. We selected all admissions from 2016 to 2018. SMRs and RSMRs were calculated by dividing the observed in-hospital mortality by the expected in-hospital mortality. The expected in-hospital mortality was calculated using logistic regression with adjustment for age, sex, socioeconomic status, severity of main diagnosis, urgency of admission, Charlson comorbidity index, place of residence before admission, month/year of admission, and in-hospital mortality as outcome.ResultsThe IQR of hospital SMRs of CVD was 0.85-1.10, median 0.94, with higher SMRs for specialized hospitals (median 1.12, IQR 1.00-1.28, 71%-SMR > 1) than for non-specialized hospitals (median 0.92, IQR 0.82-1.07, 32%-SMR > 1). The IQR of RSMRs was 0.92-1.09, median 1.00. The IQR of hospital SMRs of MI was 0.76-1.14, median 0.98, with higher SMRs for specialized hospitals (median 1.00, IQR 0.89-1.25, 50%-SMR > 1 versus median 0.94, IQR 0.74-1.11, 44%-SMR > 1). The IQR of RSMRs was 0.90-1.08, median 1.00. Adjustment for proxies of disease severity mostly led to lower SMRs of specialized hospitals.ConclusionSMRs of acute regionally organized diseases do not only measure differences in quality of care between hospitals, but merely measure differences in case-mix between hospitals. Although the addition of proxies of disease severity improves the model to calculate SMRs, real disease severity scores would be preferred. However, such scores are not available in administrative data. As a consequence, the usefulness of the current SMR as quality indicator is very limited. RSMRs are potentially more useful, since they fit regional organization and might be a more valid representation of quality of care.
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Project description:BACKGROUND:Pneumonia has a high human toll and a substantial economic burden in developed countries like Japan, where the crude mortality rate was 77.7 per 100,000 people in 2017. As this trend is going to continue with increasing number of the elderly multi-morbid population in Japan; monitoring performance over time is a social need to alleviate the disease burden. The study objective was to determine the characteristics of hospital standardized mortality ratios (HSMRs) for pneumonia in Japan from 2010 to 2018 to describe this trend. METHODS:Data of the DPC (Diagnostic Procedures Combination) database were used, which is an administrative claims and discharge summary database for acute care in-patients in Japan. HSMRs were calculated using the actual and expected numbers of in-hospital deaths, the latter of which was calculated using logistic regression model, with a number of explanatory variables, e.g., age, sex, urgency of admission, mode of transportation, patient volume per month in each hospital, A-DROP score, and Charlson comorbidity index (CCI). We constructed two HSMR models: a single-year model, which included hospitals with > 10 in-patients per month and, a 9-year model, which included those hospitals with complete 9-year data. Predictive accuracy of the logistic models was assessed using c-index (area under receiver operating curve). RESULTS:Total 230,372 patients were included for the analysis over the 9-year study period. Calculated HSMRs showed wide variation among hospitals. The proportion of hospitals with HSMR less than 100 increased from 36.4% in 2010 to 60.6% in 2018. Both models showed good predictive ability with a c-statistic of 0.762 for the 9-year model, and no less than 0.717 for the single-year model. CONCLUSION:This study denoted that HSMRs of pneumonia can be calculated using DPC data in Japan and revealed significant variations among hospitals with comparable case-mixes. Therefore, HSMR can be used as yet another measure to help improve quality of care over time if other indicators are examined in parallel and to get a clear picture of where hospitals excel and lack.
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Project description:BACKGROUND: The hospital standardized mortality ratio (HSMR) is developed to evaluate and improve hospital quality. Different methods can be used to standardize the hospital mortality ratio. Our aim was to assess the validity and applicability of directly and indirectly standardized hospital mortality ratios. METHODS: Retrospective scenario analysis using routinely collected hospital data to compare deaths predicted by the indirectly standardized case-mix adjustment method with observed deaths. Discharges from Dutch hospitals in the period 2003-2009 were used to estimate the underlying prediction models. We analysed variation in indirectly standardized hospital mortality ratios (HSMRs) when changing the case-mix distributions using different scenarios. Sixty-one Dutch hospitals were included in our scenario analysis. RESULTS: A numerical example showed that when interaction between hospital and case-mix is present and case-mix differs between hospitals, indirectly standardized HSMRs vary between hospitals providing the same quality of care. In empirical data analysis, the differences between directly and indirectly standardized HSMRs for individual hospitals were limited. CONCLUSION: Direct standardization is not affected by the presence of interaction between hospital and case-mix and is therefore theoretically preferable over indirect standardization. Since direct standardization is practically impossible when multiple predictors are included in the case-mix adjustment model, indirect standardization is the only available method to compute the HSMR. Before interpreting such indirectly standardized HSMRs the case-mix distributions of individual hospitals and the presence of interactions between hospital and case-mix should be assessed.
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Project description:BackgroundThe hospital standardised mortality ratio (HSMR), anchored at an average score of 100, is a controversial macromeasure of hospital quality. The measure may be dependent on differences in patient coding, particularly since cases labelled as palliative are typically excluded.ObjectiveTo determine whether palliative coding in Canada has changed since the 2007 national introduction of publicly released HSMRs, and how such changes may have affected results.DesignRetrospective database analysis.SettingInpatients in Canadian hospitals from April 2004 to March 2010.Patients12 593 329 hospital discharges recorded in the Canadian Institute for Health Information (CIHI) Discharge Abstract Database from April 2004 to March 2010.MeasurementsCrude mortality and palliative care coding rates. HSMRs calculated with the same methodology as CIHI. A derived hospital standardised palliative ratio (HSPR) adjusted to a baseline average of 100 in 2004-2005. Recalculated HSMRs that included palliative cases under varying scenarios.ResultsCrude mortality and palliative care coding rates have been increasing over time (p<0.001), in keeping with the nation's advancing overall morbidity. HSMRs in 2008-2010 were significantly lower than in 2004-2006 by 8.55 points (p<0.001). The corresponding HSPR rises dramatically between these two time periods by 48.83 points (p<0.001). Under various HSMR scenarios that included palliative cases, the HSMR would have at most decreased by 6.35 points, and may have even increased slightly.LimitationsInability to calculate a definitively comparable HSMR that include palliative cases and to account for closely timed changes in national palliative care coding guidelines.ConclusionsPalliative coding rates in Canadian hospitals have increased dramatically since the public release of HSMR results. This change may have partially contributed to the observed national decline in HSMR.
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Project description:BACKGROUND:Administrative data are used to examine variation in thirty-day mortality across health services in several jurisdictions. Hospital performance measurement may be error-prone as information about disease severity is not typically available in routinely collected data to incorporate into case-mix adjusted analyses. Using ischaemic stroke as a case study, we tested the extent to which accounting for disease severity impacts on hospital performance assessment. METHODS:We linked all recorded ischaemic stroke admissions between July, 2011 and June, 2014 to death registrations and a measure of stroke severity obtained at first point of patient contact with health services, across New South Wales, Australia's largest health service jurisdiction. Thirty-day hospital standardised mortality ratios were adjusted for either comorbidities, as is typically done, or for both comorbidities and stroke severity. The impact of stroke severity adjustment on mortality ratios was determined using 95% and 99% control limits applied to funnel plots and by calculating the change in rank order of hospital risk adjusted mortality rates. RESULTS:The performance of the stroke severity adjusted model was superior to incorporating comorbidity burden alone (c-statistic = 0.82 versus 0.75; N = 17,700 patients, 176 hospitals). Concordance in outlier classification was 89% and 97% when applying 95% or 99% control limits to funnel plots, respectively. The sensitivity rates of outlier detection using comorbidity adjustment compared with gold-standard severity and comorbidity adjustment was 74% and 83% with 95% and 99% control limits, respectively. Corresponding positive predictive values were 74% and 91%. Hospital rank order of risk adjusted mortality rates shifted between 0 to 22 places with severity adjustment (Median = 4.0, Inter-quartile Range = 2-7). CONCLUSIONS:Rankings of mortality rates varied widely depending on whether stroke severity was taken into account. Funnel plots yielded largely concordant results irrespective of severity adjustment and may be sufficiently accurate as a screening tool for assessing hospital performance.
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Project description:Individuals with schizophrenia or substance use disorder have a substantially increased mortality compared to the general population. Despite a high and probably increasing prevalence of comorbid substance use disorder in people with schizophrenia, the mortality in the comorbid group has been less studied and with contrasting results. We performed a nationwide open cohort study from 2009 to 2015, including all Norwegians aged 20-79 with schizophrenia and/or substance use disorder registered in any specialized health care setting in Norway, a total of 125,744 individuals. There were 12,318 deaths in the cohort, and total, sex-, age- and cause-specific standardized mortality ratios (SMRs) were calculated, comparing the number of deaths in patients with schizophrenia, schizophrenia only, substance use disorder only or a co-occurring diagnosis of schizophrenia and substance use disorder to the number expected if the patients had the age-, sex- and calendar-year specific death rates of the general population. The SMRs were 4.9 (95% CI 4.7-5.1) for all schizophrenia patients, 4.4 (95% CI 4.2-4.6) in patients with schizophrenia without substance use disorder, 6.6 (95% CI 6.5-6.8) in patients with substance use disorder only, and 7.4 (95% CI 7.0-8.2) in patients with both schizophrenia and substance use disorder. The SMRs were elevated in both genders, in all age groups and for all considered causes of death, and most so in the youngest. Approximately 27% of the excess mortality in all patients with schizophrenia was due to the raised mortality in the subgroup with comorbid SUD. The increased mortality in patients with schizophrenia and/or substance use disorder corresponded to more than 10,000 premature deaths, which constituted 84% of all deaths in the cohort. The persistent mortality gap highlights the importance of securing systematic screening and proper access to somatic health care, and a more effective prevention of premature death from external causes in this group.
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Project description:Despite recent improvements in survival after acute myocardial infarction (AMI), U.S. hospitals vary 2-fold in their 30-day risk-standardized mortality rates (RSMRs). Nevertheless, information is limited on hospital-level factors that may be associated with RSMRs.To identify hospital strategies that were associated with lower RSMRs.Cross-sectional survey of 537 hospitals (91% response rate) and weighted multivariate regression by using data from the Centers for Medicare & Medicaid Services to determine the associations between hospital strategies and hospital RSMRs.Acute care hospitals with an annualized AMI volume of at least 25 patients.Patients hospitalized with AMI between 1 January 2008 and 31 December 2009.Hospital performance improvement strategies, characteristics, and 30-day RSMRs.In multivariate analysis, several hospital strategies were significantly associated with lower RSMRs and in aggregate were associated with clinically important differences in RSMRs. These strategies included holding monthly meetings to review AMI cases between hospital clinicians and staff who transported patients to the hospital (RSMR lower by 0.70 percentage points), having cardiologists always on site (lower by 0.54 percentage points), fostering an organizational environment in which clinicians are encouraged to solve problems creatively (lower by 0.84 percentage points), not cross-training nurses from intensive care units for the cardiac catheterization laboratory (lower by 0.44 percentage points), and having physician and nurse champions rather than nurse champions alone (lower by 0.88 percentage points). Fewer than 10% of hospitals reported using at least 4 of these 5 strategies.The cross-sectional design demonstrates statistical associations but cannot establish causal relationships.Several strategies, which are currently implemented by relatively few hospitals, are associated with significantly lower 30-day RSMRs for patients with AMI.The Agency for Healthcare Research and Quality, the United Health Foundation, and the Commonwealth Fund.
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