Auteur: Jan van der Laan, Corine Witvliet-Penning, Suzanne Gerritsen, Agnes de Bruin
HSMR 2022 Methodological Report

# 1. Introduction

This report presents the methods Statistics Netherlands (CBS) has used to calculate the Hospital Standardised Mortality Ratios (HSMRs) for Dutch hospitals. HSMRs are ratios of observed and expected numbers of deaths and aim to present in-hospital mortality figures in comparison to the national average. This chapter gives a general overview of the HSMR. Chapter 2 presents the changes introduced in the method of calculating the HSMR. The methodological aspects of the model used to calculate the HSMRs are described in chapter 3. The model outcomes are evaluated in chapter 4 and include the evaluation of the model for COVID-19 admissions which was integrated in the HSMR 2022. Conclusions are given in chapter 5

## 1.1 What is the (H)SMR?

In-hospital mortality can be measured as the ratio of the number of hospital deaths to the number of hospital admissions (hospital stays) in the same period. This is generally referred to as the “gross mortality rate”. Judging hospital performance on the basis of gross mortality rates is unfair, since one hospital may have had more life-threatening cases than another. Therefore it is more appropriate to adjust (i.e. standardise) mortality rates for differences in patient characteristics (”case mix”) across hospitals as much as possible. To this end, the SMR (Standardised Mortality Ratio) of a hospital h for diagnosis d is defined as

\mathrm{SMR}_{dh} = 100 \times \frac{
\textrm{Observed mortality}_{dh}
}{
\textrm{Expected mortality}_{dh}
}

The numerator is the observed number of deaths with main diagnosis d in hospital h. The denominator is the expected number of deaths for this type of admission under the assumption that individual mortality probabilities (per admission) do not depend on the hospital, i.e. are equal to mortality probabilities of identical cases in other hospitals. The denominator is determined using a model based on data from all national hospitals, in which the in-hospital mortality is explained by patient characteristics, such as age and diagnosis, and characteristics of the admission, such as whether the admission is acute or not. Characteristics of the hospital, such as the number of doctors per bed, are generally not incorporated in the model, since these can be related to the quality of care which is the intended outcome of the indicator. The model thus produces an expected (estimated) mortality probability for each admission. Adding up these probabilities per hospital gives the total expected mortality over all admissions of that hospital. For each diagnosis d, the average SMRd across all hospitals equals 100 when each hospital is weighted with its (relative) expected mortality.

The HSMR of hospital h is defined as

\mathrm{HSMR}_{h} = 100 \times \frac{
\textrm{Observed mortality in } h
}{
\textrm{Expected mortality in } h
}

in which both the numerator and denominator are sums across all admissions for all considered diagnoses. The HSMR thus also has a weighted average of 100. As HSMRs may also deviate from 100 by chance only, confidence intervals are calculated for the SMRs and HSMRs to inform hospitals whether they have a (statistically) significantly high or low adjusted mortality rate compared with the average value of 100.

## 1.2 Purpose of the HSMR

As in many other countries, there is much interest in measuring the quality of health care in the Netherlands. Various quality indicators are available, such as the number of medical staff per bed or the availability of certain facilities. However, these indicators do not measure the outcomes of medical performance. A good indicator for the performance of a hospital is the extent to which its patients recover, given the diagnoses and other important patient characteristics, such as age, sex and comorbidity. Unfortunately, recovery is hard to measure and mostly occurs after patients have been discharged from the hospital. Although in-hospital mortality is a much more limited quality indicator, it can be measured accurately and is therefore used as a quality indicator in several countries, using the HSMR and SMRs as defined in section 1.1. If these instruments were totally valid, i.e. the calculations would adjust perfectly for everything that cannot be influenced by the hospital, a value above 100 would always indicate inferior quality of care, and the difference between numerator and denominator could be considered an estimate of “avoidable mortality”. This would only be possible if the measurement was perfect and mortality by unforeseeable complications was equally distributed across hospitals, after adjustment for differences in case mix. However, it is impossible to construct such a perfect instrument to measure the quality of health care; the outcome of the indicator will to some extent always be partially influenced by differences between hospitals with regard to case mix, availability of highly specialized treatment options, etc. A significantly high (H)SMR will at most be an indication of possible shortcomings in hospital care, but a high value may also be caused by coding errors in the data or a lack of essential covariates related to mortality in the model. Still, a significantly high (H)SMR is often seen as a warning sign and a reason for further investigation by the hospital.

## 1.3 History of the HSMR

In 1999 Jarman initiated the calculation of the (H)SMR for hospitals in England (Jarman et al., 1999). In the following years the model for estimating mortality probabilities was improved by incorporating additional covariates. Analogous models were adopted by some other countries.

In 2005, Jarman started to calculate the (H)SMR for the Netherlands. Later on, these Dutch (H)SMRs were calculated by Kiwa Prismant, in collaboration with Jarman and his colleagues of Imperial College London, Dr Foster Intelligence in London and De Praktijk Index in the Netherlands. Their method, described in Jarman et al. (2010), was slightly adapted by Prismant (Prismant, 2008) up to reporting year 2009. In 2010 DHD (Dutch Hospital Data, Utrecht), the registry holder of the national hospital discharge data, asked CBS to calculate the (H)SMRs for the period 2008-2010 and for subsequent years. CBS is an independent public body and is familiar with the input data for the HSMR, i.e. the hospital discharge register (LBZ: Landelijke Basisregistratie Ziekenhuiszorg and its predecessor LMR: Landelijke Medische Registratie), as it uses this data source for a number of health statistics (see https://opendata.cbs.nl/statline/#/CBS/nl/).

The starting point for CBS was the HSMR method previously used by Prismant. As a result of progressive insight, over the years CBS has introduced changes in the model for the HSMR, which are described in the annual methodological reports (CBS, 2011, 2012, 2013, etc.).

## 1.4 Confidentiality

Under the Statistics Netherlands Act, CBS is required to keep all data about individuals, households, companies or institutions confidential. Therefore it normally does not deliver recognisable data from institutions to third parties, unless the institutions concerned explicitly agree. For this reason, CBS needs written consent from all hospitals to deliver their hospital-specific (H)SMR figures to DHD. CBS only supplies DHD with (H)SMR outcomes of hospitals that have granted authorisation to do so. In turn, DHD sends each hospital its individual outcome report. Publication of (H)SMR data, which has become mandatory in the Netherlands since 2014 by a regulation of the Dutch Healthcare Authority (NZa), is the responsibility of the hospitals themselves. CBS does not publish data on identifiable hospitals.

## 1.5 CBS output

CBS annually estimates the models for expected mortality per diagnosis for the most recent three-year period. It calculates the HSMRs and SMRs for all hospitals that (1) had authorised CBS, (2) had registered all of its admissions in the LBZ in the relevant period, and (3) were not excluded on the grounds of criteria for data quality and comparability, which means that the hospital’s LBZ data were not too deviant in some respects (see section 3.5).

CBS produces the following output:

1. Individual hospital reports, containing their HSMR and diagnosis-specific SMR figures for the most recent reporting year and the three-year period. SMRs are also presented for different patient groups (by age, sex and urgency of admission) and for clusters of diagnoses. Hospitals can compare their outcome with the national average: overall, and per diagnosis and patient group.
2. A dataset for each hospital with the mortality probabilities for all its individual admissions. Besides the mortality probability, each admission record contains the observed mortality (0 or 1) and the scores on the covariates of the HSMR model. Hospitals can use these data for internal investigation.
3. A report on the methods used for calculating the HSMR, including the model results and parameters (this document; see www.cbs.nl).

## 1.6 Limitations of the HSMR

In section 1.2 we argued that the HSMR is not the only indicator to measure quality of hospital care. Furthermore, the quality and limitations of the HSMR (and the SMR) instrument are under debate. After all it is based on a statistical model (i.e. the denominator), which is always a simplification of reality.

Since the very first publication on the HSMR in the United Kingdom, there has been an on-going debate about the value of the HSMR as an instrument. Supporters and opponents agree that the HSMR is not a unique, ideal measure, but at most a possible indicator of the quality of health care, alongside other possible indicators. But even if HSMRs were to be used for a more limited purpose, i.e. standardising in-hospital mortality rates for unwanted side-effects, the interpretation of HSMRs would present various problems, some of which are described briefly below. See also Van Gestel et al. (2012) for an overview.

• Section 3.4 contains the list of covariates included in the regression model. Hospitals do not always code these variables in the same way. Variables such as age and sex are registered uniformly, but the registration of whether an admission was acute or not, the main discharge diagnosis or comorbidity may depend on individual physicians and coders. Lilford and Pronovost (2010) argue that if the quality of the source data is insufficient, the regression model should not adjust for such erroneously coded covariates. Our own research (Van der Laan, 2013) shows that comorbidities in particular present a problem in the Netherlands, as there is large variation in coding of this covariate (see also section 4.3). Van den Bosch et al. (2010) refer extensively to the influence of coding errors and Van Erven et al. (2018) also describe underreporting of comorbidities. Nationwide, the registration of comorbidities in Dutch hospitals has increased strongly up to 2014. From 2015 onwards the yearly increase is smaller, and in 2021 the registration of comorbidities seemed to stabilize. However, there are still hospitals with annual shifts in the registration of comorbidities. Exclusion criteria for outliers may solve this problem partly but not completely.
• Another problem is that some hospitals do not sufficiently register whether a comorbidity was a complication or not. As complications are excluded from the HSMR comorbidity covariates, underreporting complications might falsely lead to a higher comorbidity rate, thus influencing the HSMR outcomes. To stimulate correct coding of complications, an indicator has been added to the hospital HSMR reports showing the percentage of registered complications of the hospital, and the overall average. The introduction of this indicator has led to less underreporting of complications, though there are still differences in the number of complications registered by hospitals.
• Some hospitals may, on average, treat more seriously ill patients than others, even if those patients have the same set of scores on the covariates. University hospitals may, for example, have more complicated cases than other hospitals, while regional hospitals are generally more involved in end of life care. It is questionable whether the model sufficiently adjusts for factors such as severity and complexity of disease. As some of the desired covariates, such as disease stage, are not registered in the LBZ and may actually be hard to measure at all in this type of registry with routinely collected hospital discharge data, essential information to correct for differences in case mix between hospitals may be missing.
• A similar problem occurs when certain high-risk surgical procedures are only performed in a selection of hospitals. For instance, open heart surgery only occurs in authorised cardiac centres, and these hospitals may have higher SMRs for heart disease because they perform such risky interventions. This could be solved by including a covariate in the model that indicates whether such a procedure was performed. The downside however, of using a treatment method as a covariate, is that ideally it should not be part of the model as it is a component of hospital care.
• Hospital admission and discharge policies may differ between hospitals. For instance, one hospital may admit the same patient more frequently but for shorter stays than another. Or it may discharge a patient earlier due to higher availability of external terminal care facilities in the neighbourhood. Also, patients may be referred from one hospital to another for further treatment. Obviously, all these situations that mostly are unrelated to quality of care influence the observed mortality numbers and thereby the outcome of the HSMR.
• Hospitals can compare their HSMR and SMRs with the national average value of 100. A comparison of (H)SMRs between two (or more) hospitals is more complicated, as there is no complete adjustment for differences in case mix between pairs of hospitals. Theoretically, it is even possible that hospital A has higher SMRs than hospital B for all diagnosis groups, but a lower HSMR. Although this is rather theoretical, bilateral comparison of HSMRs should be undertaken with caution (Heijink et al., 2008).

Some issues in the incomplete correction for differences in case mix between hospitals may be partly addressed by peer group comparison of (H)SMRs. The calculation of (H)SMRs is still based on the model for all hospitals (without correcting for the type of hospital), but peer group comparison allows a specialised hospital to compare its results with the average for similar hospitals. For instance, the average HSMR of university hospitals is >100 in the Netherlands due to insufficient case mix correction, but comparing their results with a peer group average allows these hospitals (and for specific diagnoses also other specialised hospitals) to better interpret their own scores.

To tackle the problem relating to differences in discharge policies (e.g. the availability of terminal care outside hospital), and to some extent referrals between hospitals, an indicator of early post-discharge mortality could be included, in addition to in-hospital mortality. Ploemacher et al. (2013) observed a decrease in standardised in-hospital mortality in the Netherlands in 2005-2010, which may have been caused by an overall improvement in quality of care, but may also be partly explained by substitution of in-hospital mortality by outside-hospital mortality, possibly caused by changes in hospital admission and discharge policies. Pouw et al. (2013) performed a retrospective analysis on Dutch hospital data linked to mortality data, and concluded that including early post-discharge mortality diminishes the effect of discharge bias on the HSMR. In the UK, the SHMI (Summary Hospital-level Mortality Indicator) has been adopted, which includes mortality up to 30 days after discharge (Campbell et al., 2011). In 2014, CBS studied the optimal time frame and definition of an indicator including early post-discharge mortality (Van der Laan et al., 2015). Including all mortality within a 45-day period after admission was advised to reduce the influence of hospital discharge policies on the HSMR. A French study also recommended fixed post-admission periods of more than 30 days (Lamarche-Vadel et al., 2015).

However, including post-discharge mortality in the indicator will not reduce the effect of differences in admission policies for terminally ill patients. Some hospitals may admit more terminally ill patients to provide terminal palliative care than other hospitals and those admissions may distort HSMR outcomes. Palliative care in general can be measured in ICD-10 (code Z51.5), but this variable should be used with caution, as differences between hospitals in coding practices have been shown in e.g. the UK and Canada, and adjusting the HSMR for palliative care may increase the risk of gaming (NHS, 2013; Chong et al., 2012; Bottle et al., 2011). Because of this, and because ICD-10 code Z51.5 does not distinguish between early and terminal palliative care, palliative care admissions have not yet been excluded from the calculation of the HSMR in the Netherlands. However, the hospital HSMR reports include information on the percentage of the hospital’s admissions and deaths related to palliative care as registered in the LBZ compared to the overall average. This may indicate to some extent whether or not palliative care could have biased a hospital’s HSMR. However, since in the Netherlands there is also a large variation between hospitals in the coding of palliative care, this information should be used with caution. Because of the limitations of using code Z51.5 as an indicator of palliative care, other indicators may be considered for inclusion in future HSMR-models.

Despite the above-mentioned limitations and the ongoing debate on the validity and reliability of mortality-based indicators like the HSMR, there are studies that suggest that mortality monitoring can be indicative of failings in quality of care. In an English hospital setting, Cecil et al. (2020) found that mortality alerts, based on higher than expected mortality in 122 diagnosis and procedure groups, were associated with structural indicators of lower quality of care (e.g. lower nurse-to-bed ratio, overcrowding and financial pressures) and outcome indicators like lower patient and trainee satisfaction. They conclude that a mortality alerting system might be valuable in highlighting poor quality of care.