scholarly journals Can we trust the standardized mortality ratio? A formal analysis and evaluation based on axiomatic requirements

PLoS ONE ◽  
2021 ◽  
Vol 16 (9) ◽  
pp. e0257003
Author(s):  
Martin Roessler ◽  
Jochen Schmitt ◽  
Olaf Schoffer
Keyword(s):  
Mortality Rates ◽  
Hospital Performance ◽  
Case Mix ◽  
Standardized Mortality Ratio ◽  
Strict Monotonicity ◽  
Hospital Size ◽  
Mortality Ratio ◽  
Dominance Principle ◽  
Internal Standardization ◽  
Expected Mortality

Background The standardized mortality ratio (SMR) is often used to assess and compare hospital performance. While it has been recognized that hospitals may differ in their SMRs due to differences in patient composition, there is a lack of rigorous analysis of this and other—largely unrecognized—properties of the SMR. Methods This paper proposes five axiomatic requirements for adequate standardized mortality measures: strict monotonicity (monotone relation to actual mortality rates), case-mix insensitivity (independence of patient composition), scale insensitivity (independence of hospital size), equivalence principle (equal rating of hospitals with equal actual mortality rates in all patient groups), and dominance principle (better rating of unambiguously better performing hospitals). Given these axiomatic requirements, effects of variations in patient composition, hospital size, and actual and expected mortality rates on the SMR were examined using basic algebra and calculus. In this regard, we distinguished between standardization using expected mortality rates derived from a different dataset (external standardization) and standardization based on a dataset including the considered hospitals (internal standardization). The results were illustrated by hypothetical examples. Results Under external standardization, the SMR fulfills the axiomatic requirements of strict monotonicity and scale insensitivity but violates the requirement of case-mix insensitivity, the equivalence principle, and the dominance principle. All axiomatic requirements not fulfilled under external standardization are also not fulfilled under internal standardization. In addition, the SMR under internal standardization is scale sensitive and violates the axiomatic requirement of strict monotonicity. Conclusions The SMR fulfills only two (none) out of the five proposed axiomatic requirements under external (internal) standardization. Generally, the SMRs of hospitals are differently affected by variations in case mix and actual and expected mortality rates unless the hospitals are identical in these characteristics. These properties hamper valid assessment and comparison of hospital performance based on the SMR.

Trends in COVID-19 Risk-Adjusted Mortality Rates

2020 ◽  
Author(s):  
Leora I Horwitz ◽  
Simon A Jones ◽  
Robert J Cerfolio ◽  
Fritz Francois ◽  
Joseph Greco ◽  
...  
Keyword(s):  
New York ◽  
Hospital Mortality ◽  
Health System ◽  
Vital Signs ◽  
Patient Characteristics ◽  
Mortality Rates ◽  
Standardized Mortality Ratio ◽  
Marginal Effect ◽  
Average Probability ◽  
Mortality Ratio

Early reports showed high mortality from coronavirus disease 2019 (COVID-19). Mortality rates have recently been lower, raising hope that treatments have improved. However, patients are also now younger, with fewer comorbidities. We explored whether hospital mortality was associated with changing demographics at a 3-hospital academic health system in New York. We examined in-hospital mortality or discharge to hospice from March through August 2020, adjusted for demographic and clinical factors, including comorbidities, admission vital signs, and laboratory results. Among 5,121 hospitalizations, adjusted mortality dropped from 25.6% (95% CI, 23.2-28.1) in March to 7.6% (95% CI, 2.5-17.8) in August. The standardized mortality ratio dropped from 1.26 (95% CI, 1.15-1.39) in March to 0.38 (95% CI, 0.12-0.88) in August, at which time the average probability of death (average marginal effect) was 18.2 percentage points lower than in March. Data from one health system suggest that mortality from COVID-19 is decreasing even after accounting for patient characteristics.

USE OF CODED MORTALITY DATA TO ASSESS AREA CANCER RATES: IMPACT OF RESIDENCE REPORTING AND CODING ERRORS

1990 ◽  
Vol 132 (supp1) ◽  
pp. 178-182 ◽  
Author(s):  
ALLAN N. WILLIAMS ◽  
REBECCA A. JOHNSON ◽  
ALAN P. BENDER
Keyword(s):  
Rural Areas ◽  
Cancer Mortality ◽  
Mortality Rates ◽  
Mortality Data ◽  
Standardized Mortality Ratio ◽  
Death Certificates ◽  
Total Cancer ◽  
Mortality Ratio ◽  
Cancer Rates ◽  
Standardized Mortality Ratios

Abstract In spite of their limitations, mortality data are used in many epidemiologic and public health settings. In this investigation, the authors examined the extent to which community cancer mortality rates were affected by incorrect reporting or coding of residence on death certificates. Observed and expected cancer mortality for two adjacent communities in northern rural Minnesota for the periods 1970–1974 and 1980–1984 were obtained from computerized state mortality data. Using statewide rates to obtain expected values, standardized mortality ratios for total cancers for both periods combined were 138 for men (101 observed deaths) and 148 for women (86 observed deaths). These excesses were statistically significant (p < 0.05). However, after review of data from the actual death certificates, city maps, and information from city officials, 44 of the 187 total cancer deaths (24%) were found to have had an incorrectly reported or coded residence status. After removal of these cases, the standardized mortality ratio for total cancers for males went from 138 to 107, and for females the standardized mortality ratio went from 148 to 111. No standardized mortality ratios remained statistically significant These findings may have implications for those who use mortality data for assessing cancer rates in communities in rural areas.

scholarly journals Socioeconomic Disparities in All-Cause and Cause-Specific Mortality Rates among Municipalities in Japan, 1999–2019

2020 ◽  
Vol 17 (24) ◽  
pp. 9213
Author(s):  
Tasuku Okui
Keyword(s):  
Mortality Rates ◽  
Vital Statistics ◽  
Socioeconomic Disparities ◽  
Standardized Mortality Ratio ◽  
Low Ses ◽  
All Cause Mortality ◽  
Specific Mortality ◽  
Cause Of Deaths ◽  
Mortality Ratio ◽  
The Difference

Differences in all-cause and cause-specific mortality rates depending on municipal socioeconomic status (SES) in Japan have not been revealed over the last 20 years. This study exposes the difference in 1999 and 2019 using the Vital Statistics. All of the municipalities were grouped into five quintiles based on their SES, and standardized mortality ratio (SMR) of each municipal quintile compared with all of Japan was calculated for all-cause mortality and representative cause of deaths. As a result, although SMR for all-cause mortality for women tended to be lower in low SES quintiles in 1999, the reverse phenomenon was observed in 2019. Additionally, although SMR for all-cause of mortality for men was the lowest in the highest SES quintiles already in 1999, the difference in the SMR for all-cause mortality rates between the lowest and highest SES quintiles increased in 2019. The improvement of the SMR in the highest SES quintile and the deterioration in the lowest was also observed in representative types of cancer, heart disease, stroke, pneumonia, liver disease, and renal failure for men and women. Therefore, this study indicates a disparity in mortality depending on municipal SES enlarged in the last 20 years.

Effect of Varying the Case Mix on the Standardized Mortality Ratio and W Statistic

CHEST Journal ◽  
2000 ◽  
Vol 117 (4) ◽  
pp. 1112-1117 ◽  
Author(s):  
Laurent G. Glance ◽  
Turner Osler ◽  
Tamotsu Shinozaki
Keyword(s):  
Case Mix ◽  
Standardized Mortality Ratio ◽  
Mortality Ratio

Hospital variation in downstream secondary preventive medication uptake: implications for quality and outcomes

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
B Jug ◽  
P Dosenovic Bonca ◽  
Z Fras
Keyword(s):  
Myocardial Infarction ◽  
Hospital Performance ◽  
Cox Proportional Hazards ◽  
Case Mix ◽  
Hospital Size ◽  
Risk Of Death ◽  
Hospital Variation ◽  
Preventive Medication ◽  
High Adherence ◽  
Hospital Characteristics

Abstract Background Adherence to guideline recommended therapy improves outcomes in patients with myocardial infarction. We sought to appraise between-hospital variation in downstream adherence to secondary preventive medication and hypothesised that discharge from high-adherence hospitals was associated with improved long-term outcomes. Methods We included 7,624 patients hospitalised because of myocardial infarction in 14 hospitals between 2015 and 2017 in Slovenia (nation-wide sample). Using indirect identifiers, we linked routinely collected data on patients from the i) Hospital Discharge Statistics Database, ii) the National Mortality Registry and iii) the Medication Reimbursement Database. We used random intercept logistic regression modelling for prediction of secondary prevention medication uptake 30 to 90 days after discharge, adjusting for case-mix, hospital size and percutaneous coronary intervention (PCI) availability. We classified hospitals into high- and low adherence (i.e., above and below the national median adjusted odds) and assessed the association between hospital performance, and death or re-infarction with multivariate Cox proportional hazards modelling. Results Secondary preventive medication uptake ranged from 30.7% to 87.3% in high- and low-performing hospitals, respectively. Discharge from high-adherence hospitals were associated with significantly lower all-cause mortality and re-infarction rates (hazard ratio 0.82; 95% confidence interval 0.69–0.98) when compared to hospitals with secondary preventive medication uptake below the national median, after adjusting for case-mix and hospital characteristics. Conclusions Downstream secondary preventive medication uptake vary across hospitals. Although case-mix and hospital characteristics explain some of the variation, discharge from a hospital with good downstream secondary preventive medication uptake is associated with lower risk of death or re-infarction. Event rates, low- vs. high-adhererence Funding Acknowledgement Type of funding source: None

Vital Records for Quality Improvement

PEDIATRICS ◽  
1999 ◽  
Vol 103 (Supplement_E1) ◽  
pp. 278-290 ◽  
Author(s):  
Jeffrey B. Gould
Keyword(s):  
Quality Improvement ◽  
Birth Weight ◽  
Neonatal Mortality ◽  
Death Certificate ◽  
Birth Certificate ◽  
Population Based ◽  
Standardized Mortality Ratio ◽  
Vital Records ◽  
Mortality Ratio ◽  
Expected Mortality

The birth certificate and death certificate are important sources of population-based data for assessing the extent of risk and the quality of perinatal outcome. The birth certificate contains the hospital of birth and many items, such as birth weight and race, that can serve as important risk adjusters for neonatal mortality. To assess mortality a second vital record, the death certificate, must be linked to the birth certificate. If the analysis is to be stratified by level of neonatal care or other hospital characteristics, a third file providing these details must also be utilized. The exact vital record formats, recording protocols, and quality control efforts are determined by and differ across each state. Even with these differences, the quality and completeness of vital records and their linkage are reasonable for population-based analyses. Although the most important vital outcome from a neonatologist's perspective is neonatal mortality, vital records can also be used to assess fetal, perinatal, postneonatal, and infant mortality. The analytic paradigm that is used in quality analysis performed on data derived from the vital record states that observed outcome is a function of risk, chance, and care. Risk is a characteristic or condition such as low birth weight or low 1-minute Apgar score that elevates the probability of an adverse outcome but is beyond the control of the agent responsible for the outcome. Using risk matrices or regression analysis one determines the expected mortality for a specific institution's case-mix. This expectation is usually based on the statewide analysis of infants with a similar risk profile. A standardized mortality ratio is calculated by dividing observed by expected mortality. A hospital with a high observed mortality (12 deaths per 1000) and an even higher expected mortality based on the risk characteristics of its neonates (24 per 1000) would have a standardized mortality ratio of 0.5. Once the effects of chance have been accounted for by statistical testing this finding could indicate that mortality in this hospital is 50% lower then expected. Although initially intended for legal and broad-based public health purposes, vital records represent an important source of data to inform perinatal quality improvement activities. The optimal usefulness of information derived from vital records requires that clinicians take an active role in assuring that data entry is complete and accurately reflects risk status, clinical factors, and outcomes. However, even a superb database will be of limited usefulness unless it is linked to an initiative that actively involves clinicians committed to quality improvement.

EFFECT OF CHANGING THE CASE-MIX ON THE STANDARDIZED MORTALITY RATIO

1999 ◽  
Vol 27 (Supplement) ◽  
pp. 64A
Author(s):  
Laurent Glance ◽  
Turner Osler
Keyword(s):  
Case Mix ◽  
Standardized Mortality Ratio ◽  
Mortality Ratio

Measures of disease occurrence

2020 ◽  
pp. 41-46
Author(s):  
Bendix Carstensen
Keyword(s):  
Mortality Rate ◽  
General Population ◽  
Incidence Rate ◽  
Mortality Rates ◽  
Incidence Rates ◽  
Standardized Mortality Ratio ◽  
Newly Diagnosed ◽  
Disease Occurrence ◽  
Mortality Ratio ◽  
Incident Cases

This chapter provides a brief introduction to some of the most common measures of disease occurrence used in epidemiology, both the empirical and theoretical versions of the measures. It begins with the prevalence of a disease in a population, which is the fraction of the population that has the disease at a given date. The chapter then considers mortality rate, incidence rate, standardized mortality ratio (SMR), and survival. Mortality is typically reported as a number of people that have died in a population of a certain size. Incidence rates are defined exactly as mortality rates, where one just counts incident cases, that is, newly diagnosed cases of a particular disease. Meanwhile, the SMR is a measure of the mortality in a group of persons as compared to the general population. Finally, the survival after diagnosis of a disease is defined as the fraction of diagnosed individuals alive at a given time after diagnosis.

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