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30-Day Survival Probabilities as a Quality Indicator for Norwegian Hospitals: Data Management and Analysis.

Hassani S, Lindman AS, Kristoffersen DT, Tomic O, Helgeland J - PLoS ONE (2015)

Bottom Line: A multiple testing procedure with a false discovery rate of 5% is used to identify hospitals, hospital trusts and regional health authorities with significantly higher/lower survival than the reference.Since 2011, several hospitals and hospital trusts have initiated quality improvement projects, and some of the hospitals have improved the survival over these years.In this way, the methods employed for establishing the indicators may be improved.

View Article: PubMed Central - PubMed

Affiliation: Norwegian Knowledge Centre for the Health Services, Oslo, Norway; Department of Medical Genetics, University of Oslo and Oslo University Hospital, Oslo, Norway; NORMENT, KG Jebsen Centre for Psychosis Research, Oslo University Hospital, Oslo, Norway.

ABSTRACT

Background: The Norwegian Knowledge Centre for the Health Services (NOKC) reports 30-day survival as a quality indicator for Norwegian hospitals. The indicators have been published annually since 2011 on the website of the Norwegian Directorate of Health (www.helsenorge.no), as part of the Norwegian Quality Indicator System authorized by the Ministry of Health. Openness regarding calculation of quality indicators is important, as it provides the opportunity to critically review and discuss the method. The purpose of this article is to describe the data collection, data pre-processing, and data analyses, as carried out by NOKC, for the calculation of 30-day risk-adjusted survival probability as a quality indicator.

Methods and findings: Three diagnosis-specific 30-day survival indicators (first time acute myocardial infarction (AMI), stroke and hip fracture) are estimated based on all-cause deaths, occurring in-hospital or out-of-hospital, within 30 days counting from the first day of hospitalization. Furthermore, a hospital-wide (i.e. overall) 30-day survival indicator is calculated. Patient administrative data from all Norwegian hospitals and information from the Norwegian Population Register are retrieved annually, and linked to datasets for previous years. The outcome (alive/death within 30 days) is attributed to every hospital by the fraction of time spent in each hospital. A logistic regression followed by a hierarchical Bayesian analysis is used for the estimation of risk-adjusted survival probabilities. A multiple testing procedure with a false discovery rate of 5% is used to identify hospitals, hospital trusts and regional health authorities with significantly higher/lower survival than the reference. In addition, estimated risk-adjusted survival probabilities are published per hospital, hospital trust and regional health authority. The variation in risk-adjusted survival probabilities across hospitals for AMI shows a decreasing trend over time: estimated survival probabilities for AMI in 2011 varied from 80.6% (in the hospital with lowest estimated survival) to 91.7% (in the hospital with highest estimated survival), whereas it ranged from 83.8% to 91.2% in 2013.

Conclusions: Since 2011, several hospitals and hospital trusts have initiated quality improvement projects, and some of the hospitals have improved the survival over these years. Public reporting of survival/mortality indicators are increasingly being used as quality measures of health care systems. Openness regarding the methods used to calculate the indicators are important, as it provides the opportunity of critically reviewing and discussing the methods in the literature. In this way, the methods employed for establishing the indicators may be improved.

No MeSH data available.


Related in: MedlinePlus

Distribution of estimated risk-adjusted 30-day survival probabilities for the hospital-wide indicator across hospitals.Distribution plots for the risk-adjusted survival probabilities are colored in red, green and blue for the years 2011, 2012 and 2013 respectively. The dotted lines show the reference value for each year.
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pone.0136547.g004: Distribution of estimated risk-adjusted 30-day survival probabilities for the hospital-wide indicator across hospitals.Distribution plots for the risk-adjusted survival probabilities are colored in red, green and blue for the years 2011, 2012 and 2013 respectively. The dotted lines show the reference value for each year.

Mentions: The Norwegian Knowledge Centre for the Health Services has published survival indicators annually since 2011 at hospital, hospital trust and regional health authority level [10–12]. The results are summarized in Tables 3–5. Total number of hospitals, hospital trusts and regional health authorities as well as the number of those with significantly higher/lower survival (compare to the reference) for each indicator category are given in the above mentioned tables. The highest, lowest and the reference for the risk-adjusted estimated survival probabilities calculated for the hospitals over the last three years are given in Table 6. Interestingly, it may be noticed that variation in estimated risk-adjusted survival indicators i.e. the difference between the highest and lowest estimated survival probabilities for each year, has shown a decreasing trend over time (although not statistically tested). This observed decreasing trend is visualised in Figs 3 and 4, where the distribution of estimated risk-adjusted 30-day survival probabilities for AMI (as an example of the diagnosis specific indicators) and hospital-wide indicator are plotted. The reference values for these two survival indicators show an increasing trend over time (see Figs 3 and 4), indicating increased survival over time.


30-Day Survival Probabilities as a Quality Indicator for Norwegian Hospitals: Data Management and Analysis.

Hassani S, Lindman AS, Kristoffersen DT, Tomic O, Helgeland J - PLoS ONE (2015)

Distribution of estimated risk-adjusted 30-day survival probabilities for the hospital-wide indicator across hospitals.Distribution plots for the risk-adjusted survival probabilities are colored in red, green and blue for the years 2011, 2012 and 2013 respectively. The dotted lines show the reference value for each year.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4564217&req=5

pone.0136547.g004: Distribution of estimated risk-adjusted 30-day survival probabilities for the hospital-wide indicator across hospitals.Distribution plots for the risk-adjusted survival probabilities are colored in red, green and blue for the years 2011, 2012 and 2013 respectively. The dotted lines show the reference value for each year.
Mentions: The Norwegian Knowledge Centre for the Health Services has published survival indicators annually since 2011 at hospital, hospital trust and regional health authority level [10–12]. The results are summarized in Tables 3–5. Total number of hospitals, hospital trusts and regional health authorities as well as the number of those with significantly higher/lower survival (compare to the reference) for each indicator category are given in the above mentioned tables. The highest, lowest and the reference for the risk-adjusted estimated survival probabilities calculated for the hospitals over the last three years are given in Table 6. Interestingly, it may be noticed that variation in estimated risk-adjusted survival indicators i.e. the difference between the highest and lowest estimated survival probabilities for each year, has shown a decreasing trend over time (although not statistically tested). This observed decreasing trend is visualised in Figs 3 and 4, where the distribution of estimated risk-adjusted 30-day survival probabilities for AMI (as an example of the diagnosis specific indicators) and hospital-wide indicator are plotted. The reference values for these two survival indicators show an increasing trend over time (see Figs 3 and 4), indicating increased survival over time.

Bottom Line: A multiple testing procedure with a false discovery rate of 5% is used to identify hospitals, hospital trusts and regional health authorities with significantly higher/lower survival than the reference.Since 2011, several hospitals and hospital trusts have initiated quality improvement projects, and some of the hospitals have improved the survival over these years.In this way, the methods employed for establishing the indicators may be improved.

View Article: PubMed Central - PubMed

Affiliation: Norwegian Knowledge Centre for the Health Services, Oslo, Norway; Department of Medical Genetics, University of Oslo and Oslo University Hospital, Oslo, Norway; NORMENT, KG Jebsen Centre for Psychosis Research, Oslo University Hospital, Oslo, Norway.

ABSTRACT

Background: The Norwegian Knowledge Centre for the Health Services (NOKC) reports 30-day survival as a quality indicator for Norwegian hospitals. The indicators have been published annually since 2011 on the website of the Norwegian Directorate of Health (www.helsenorge.no), as part of the Norwegian Quality Indicator System authorized by the Ministry of Health. Openness regarding calculation of quality indicators is important, as it provides the opportunity to critically review and discuss the method. The purpose of this article is to describe the data collection, data pre-processing, and data analyses, as carried out by NOKC, for the calculation of 30-day risk-adjusted survival probability as a quality indicator.

Methods and findings: Three diagnosis-specific 30-day survival indicators (first time acute myocardial infarction (AMI), stroke and hip fracture) are estimated based on all-cause deaths, occurring in-hospital or out-of-hospital, within 30 days counting from the first day of hospitalization. Furthermore, a hospital-wide (i.e. overall) 30-day survival indicator is calculated. Patient administrative data from all Norwegian hospitals and information from the Norwegian Population Register are retrieved annually, and linked to datasets for previous years. The outcome (alive/death within 30 days) is attributed to every hospital by the fraction of time spent in each hospital. A logistic regression followed by a hierarchical Bayesian analysis is used for the estimation of risk-adjusted survival probabilities. A multiple testing procedure with a false discovery rate of 5% is used to identify hospitals, hospital trusts and regional health authorities with significantly higher/lower survival than the reference. In addition, estimated risk-adjusted survival probabilities are published per hospital, hospital trust and regional health authority. The variation in risk-adjusted survival probabilities across hospitals for AMI shows a decreasing trend over time: estimated survival probabilities for AMI in 2011 varied from 80.6% (in the hospital with lowest estimated survival) to 91.7% (in the hospital with highest estimated survival), whereas it ranged from 83.8% to 91.2% in 2013.

Conclusions: Since 2011, several hospitals and hospital trusts have initiated quality improvement projects, and some of the hospitals have improved the survival over these years. Public reporting of survival/mortality indicators are increasingly being used as quality measures of health care systems. Openness regarding the methods used to calculate the indicators are important, as it provides the opportunity of critically reviewing and discussing the methods in the literature. In this way, the methods employed for establishing the indicators may be improved.

No MeSH data available.


Related in: MedlinePlus