Case-Mix Adjustment for Diabetes Indicators: A Systematic Review

ABSTRACT

Objectives: Case-mix adjustment is generally considered indispensable for fair comparison of healthcare performance. Inaccurate results are also unfair to patients as they are ineffective for improving quality. However, little is known about what factors should be adjusted for. We reviewed case-mix factors included in adjustment models for key diabetes indicators, the rationale for their inclusion, and their impact on performance.

Study Design: Systematic review.

Methods: This systematic review included studies published up to June 2013 addressing case-mix factors for 6 key diabetes indicators: 2 outcomes and 2 process indicators for glycated hemoglobin (A1C), low-density lipoprotein cholesterol, and blood pressure. Factors were categorized as demographic, diabetes-related, comorbidity, generic health, geographic, or care-seeking, and were evaluated on the rationale for inclusion in the adjustment models, as well as their impact on indicator scores and ranking.

Results: Thirteen studies were included, mainly addressing A1C value and measurement. Twenty-three different case-mix factors, mostly demographic and diabetes-related, were identified, and varied from 1 to 14 per adjustment model. Six studies provided selection motives for the inclusion of case-mix factors. Marital status and body mass index showed a significant impact on A1C value. For the other factors, either no or conflicting associations were reported, or too few studies (n ≤2) investigated this association.

Conclusions: Scientific knowledge about the relative importance of case-mix factors for diabetes indicators is emerging, especially for demographic and diabetes-related factors and indicators on A1C, but is still limited. Because arbitrary adjustment potentially results in inaccurate quality information, meaningful stratification that demonstrates inequity in care might be a better guide, as it can be a driver for quality improvement.

Am J Manag Care. 2016;22(2):e45-e52

Take-Away Points

Diabetes is associated with elevated morbidity, comorbidities, complications, and early mortality rates. It has an increasingly high prevalence and correspondingly high healthcare costs. Benchmarking is expected to reflect differences in quality of care, but case-mix factors can affect these scores as well. This review provides an overview of case-mix factors that are ideally included for specific indicators, showing:

• A large variation in case-mix factors is used, and the factors’ impact varies greatly.
• Rationales for the selection of case-mix factors are rarely provided.
• More evidence-based knowledge on case-mix adjustment for diabetes quality indicators is necessary for fair quality-of-care comparisons.

There is an increasing quest for public information on the quality of healthcare, which increases the need for transparency of the performance of individual healthcare providers as well as hospitals. For this purpose, the use of quality indicators has widely been adopted. Among the disease entities with ample research on quality indicators is diabetes. Treating and monitoring patients with diabetes is challenging, as the disease is associated with elevated morbidity, comorbidities, complications, and early mortality rates. It has an increasingly high prevalence, and healthcare costs are correspondingly high.^1-3 Benchmarking performances in diabetes care is expected to reflect differences in quality of care delivered, but it is commonly known that population characteristics such as comorbidities or age can affect these scores as well. To facilitate fair comparison of quality of care, case-mix adjustment based on these population characteristics is suggested for adequate public reporting on healthcare provider performance.⁴ Indeed, the results of several studies indicate that the ranking of hospitals and physicians might change after adjustment by case-mix factors.^5-7 Other studies, however, do not report clear effects.^8,9

These conflicting results can be explained by the variety of factors used in the different risk-adjustment models.¹⁰ This variety may exist due to a lack of knowledge of, or consensus on, factors that need to be included in these models; nevertheless, knowledge of factors that could affect the quality of care does not axiomatically mean that we should adjust quality indicators for these factors. Ten years ago, Chin⁴ outlined the key elements of a diabetes case-mix adjustment tool based on the scientific knowledge so far at that time. One of these elements is validity, meaning that case-mix adjustment tools should do the required task of adjusting for factors affecting glycated hemoglobin (A1C) value beyond the provider’s reasonable control. For example, when female patients or patients from minority ethnic groups receive lower quality of care and the performance can be controlled by the healthcare provider, gender and ethnicity should not be included in a risk-adjustment model.⁴ Instead, this gender- and ethnicity-specific information should be made explicit (eg, by calculating stratified quality indicator scores). This is just a single element of an appropriate case-mix adjustment tool. In addition, a case-mix tool developed for a single end point, such as A1C value, is not necessarily valid for other diabetes-related outcomes. Another recommended criterion is clinical face validity, meaning that the case-mix adjustment tool should comprehend all key factors. Also, Chin⁴ refers to a conceptualization of 6 fundamental domains (demographics, access to care, healthcare-seeking behavior, geographic location, duration and severity of diabetes, and comorbidity),¹¹ which is a good start to ensure incorporation of relevant variables, and thus, clinical face validity. Other recommended characteristics of a case-mix adjustment tool are feasibility (ie, the tool should be simple to use and affordable) and applicability (ie, the tool should be applicable to a specific target population of patients with diabetes).

In order to assess the current scientific knowledge on diabetes case-mix adjustment and to advance an appropriate and valid case-mix adjustment, researchers and quality assessors may benefit from an overview of the evidence for inclusion of case-mix factors for specific indicators. This is also relevant for healthcare providers because only appropriate case-mix adjustment allows comparisons and benchmarking among providers. Furthermore, more insight into relevant case-mix factors may facilitate registration needs, as only context information that matters needs to be collected.

We therefore aimed to review the literature to: a) identify relevant case-mix factors for the 6 most commonly used quality indicators for diabetes (namely, the outcome and process indicators for A1C value, low-density lipoprotein cholesterol [LDL-C] value, and blood pressure)¹²; b) to explore the rationale for the selection of these factors; and c) to study the impact of these factors on performance (ie, diabetes quality indicator scores).

METHODS

Data Sources and Searches

A systematic literature search was performed in the electronic databases PubMed, EMBASE, and the Cochrane Library on June 5, 2013, to identify scientific publications specifically addressing case-mix adjustment for diabetes quality indicators. The search was performed using the terms “diabetes mellitus,” “quality indicators,” “risk adjustment,” and “case-mix,” as well as combinations of these terms and related Mesh terms (eAppendix Table 1 [eAppendices available at www.ajmc.com]). The search was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines.^13,14

Study Selection

We included original research explicitly addressing the impact of case-mix adjustment in diabetes care quality research (studies that both included adults and were written in English), with regard to 3 outcome indicators—A1C value (% or mmol/mol), LDL-C value (mg/dL), and blood pressure (mmHg)—and 3 process indicators—A1C measurements (% patients), LDL-C measurements (% patients), and blood pressure measurements (% patients).

Studies were excluded when they: a) addressed the quality of care provided to specific subgroups of patients with diabetes (eg, older patients) because these studies provide information on possible voids in provided quality of care to these subgroups rather than providing information on the relevance of including such factors in case-mix adjustment models; or b) applied case-mix factors as covariates to correct for variability between groups (intervention studies or trend studies).

The studies were first screened independently for eligibility based on title and abstract, by 2 reviewers (HC, GEV). In the case of disagreement, the publication was included in the first step of the selection process (see Figure). Next, full-text copies were reviewed by the same 2 reviewers and a decision was made for final inclusion or exclusion. If the 2 reviewers disagreed on inclusion, a third reviewer (JCCB) was consulted. The list of included studies was completed by hand-searching the reference lists of the selected studies and the journals in which these studies were published.

Data Extraction and Quality Assessment

From each included study, we extracted information on: aim; study design; study population; quality indicators; case-mix factors and their rationale for inclusion; the statistical association between case-mix factor and quality indicator; and, if available, the percentage, which explained variance and the effect of case-mix correction on ranking. Two reviewers (HC, JGMM) independently assessed the methodological quality of the studies and discussed their results for consensus. Using the Meta-Analysis of Observational Studies in Epidemiology guideline,¹⁴ the methodological quality was evaluated using the following criteria (which were slightly rephrased to fit with the content of the current study): background and rationale reported, objectives and hypotheses stated, study design described, eligibility criteria specified, selection of sample described, selection method of case-mix factors described, and statistical methods described.

Data Synthesis and Analysis

First, case-mix factors were categorized into the fundamental domains as described by Zhang et al¹¹: demographic factors (eg, age, gender), diabetes-related factors (eg, duration of diabetes), comorbidity factors, geographic deprivation, and care-seeking factors (eg, free medical care). We added the domain “generic health” to classify the factors of health status and health status and quality of life, as used in several studies.

Then, we categorized the rationales for inclusion of case-mix factors according to the key elements of an ideal case-mix tool, as outlined by Chin,⁴ who stated that the tool: a) should be valid, implying that the tool adjusts for factors affecting the particular indicator beyond the provider’s reasonable control; b) needs to have clinical face validity, meaning that it does not lack key factors. With such validity, incorporation of relevant factors is ensured and the factor does not wrongly pardon a difference in the quality of care for which the provider should be accountable (eg, see gender example in introduction section); c) must be feasible, in terms of simplicity and affordability; and d) must be applicable to the target population; that is, the tool is developed on a similar set of patients.

Finally, we studied the impact of the case-mix variables on outcome. A factor was thought to have impact on outcome when an association between factor and indicator was shown in at least 3 studies, and no study reported otherwise. A similar reasoning was adopted for defining no impact. Impact was considered inconsistent when different studies reported both the presence and absence of an association between factor and indicator.

RESULTS

Selection of Studies

After applying the selection criteria, 13 studies were included in the review (Figure).

Characteristics and Quality Assessment of the Studies

eAppendix Table 2 provides a comprehensive overview of the study characteristics. In summary (see also eAppendix Table 3), the 13 studies were published between 1998 and 2012. Most of the studies were conducted in the Unites States (n = 10), 2 in the United Kingdom, and 1 in The Netherlands, and each of them had a retrospective observational design. The study samples ranged from 208 to 118,167 patients with diabetes.^6,15 Six studies reported on both process and outcome indicators,^{7,8,16,18,20,21} 6 on outcome indicators,^{5,6,9,11,15,19} and 1 on process indicators,¹⁷ with the outcome indicator for A1C value being the most frequently studied. Each study reported on more than 1 case-mix variable (see also next section). Effects of case-mix adjustment were studied across facilities^{5-8,11,15,16,18,20,21} or individual physicians.^9,19 One study reported effects on both levels.¹⁶ The criterion “selection method of case-mix variables described” could be applied to 6 studies,^{5,6,8,9,11,17} while the other quality criteria were satisfied by all studies (except the study design description¹⁷).

Case-Mix Factors Included in Adjustment Models and Rationales for Their Selection

Table 1 provides an overview of the factors included in case-mix adjustment models for quality indicator scores, stratified by domain and indicator. Altogether, 23 different case-mix factors were identified in the 13 studies. Demographic factors, especially age and gender, were most frequently included, followed by diabetes duration and comorbidity. The more extensive and diverse case-mix models were found for the outcome indicators, in particular for A1C value. The rationale for selection of case-mix factors was reported in 6 studies (eAppendix Table 3),^{5,6,8,9,11,17} mainly covering the elements of clinical face validity (eg, evidence from other studies) and feasibility (eg, data availability).

Impact of Case-Mix Factors: Outcome Indicators

A1C value. Twelve studies investigated case-mix factors for this quality indicator. Impact on indicator scores (Table 2) were found for marital status (demographic domain) and body mass index (BMI; diabetes-related domain); married patients with diabetes^5-7 and those with lower BMI^5,7,15 generally had lower A1C values compared with patients who were not married and those with higher BMI. For the factors of age,^{5-9,11,15,16,18-21} gender,^{5,7-9,11,18-21} ethnicity,^7,11,21 education,^5,9,19-21 diabetes duration,^5,9,11,19-21 comorbidity,^5,7,8,11,16 and health status,^5,9,19,21 inconsistent impact on outcome was reported. Other factors were rarely (n ≤2) studied. The percentages explained variance (eAppendix Table 2) induced by case-mix correction were generally small (3%-10.5%).^{5,9,11,16,18,19} Effects of case-mix correction on ranking particularly affected outliers and bottom- and top-performing hospitals in 3 studies.^5,6,11 Joish et al⁷ reported that after case-mix correction, clear differences among the 4 participating facilities emerged, while Berlowitz et al¹⁵ showed that case-mix correction reduced differences among facilities. Four studies were unable to demonstrate ranking effects after case-mix adjustment for A1C value.^8,9,20,21

LDL-C value. Six studies investigated case-mix factors for this indicator^7-9,16,20,21: inconsistent impact was found for the factors of age, gender, and comorbidity^7-9,16,20,21; no association was found for diabetes duration.^9,20,21 Other factors were rarely (n ≤2) studied and no data on explained variance was reported. Two studies showed a ranking effect of case-mix correction on facilities and healthcare providers,^7,21 while 1 study showed no significant difference in ranking after case-mix adjustment.¹⁶

Blood pressure. No impact was found for age,^8,9,20,21 gender,^8,9,20,21 education,^9,11,20 and diabetes duration.^9,20,21 Other factors (as mentioned in Tables 1 and 2) were rarely (n ≤2) studied. No data on explained variance were reported. Although Greenfield et al²¹ demonstrated significant differences between unadjusted and adjusted indicator scores across healthcare providers, Gorter et al²⁰ were unable to detect a difference.

Impact of Case-Mix Factors: Process Indicators

A1C measurement. Eight studies investigated case-mix factors for this indicator (Table 2).^{7,8,11,12,15,16,19,20} The impact of age^{7,8,16,18,20,21} and comorbidity^7,8,16,17 was inconsistent across studies, while no impact was found for gender.^7,8,11,18,20 Other factors (as mentioned in Tables 1 and 2) were rarely (n ≤2) studied. In terms of explained variance, the difference in adjusted versus unadjusted results was 2.6% in 1 study on plan level.¹⁷ An increase of comorbidity from 5% to 10% in patients with type 2 diabetes appeared to be associated with an increase of 1.3% in the screening rate of A1C. The regression model (using Healthcare Effectiveness Data and Information Set data) also included the total number of enrollees with diabetes, health plan type, and regions, overall explaining 24.2% of variation across health plans. No other data on explained variance or ranking were reported.^8,16,18,21

LDL-C measurement. Three studies investigated the impact of case-mix on this process indicator.^8,16,17 The impact of comorbidity was found to be absent.^8,16,17 Abraham et al¹⁷ found that the overall regression model on LDL-C measurement explained 26.9% of variation across health plans. No other data on explained variance or ranking were reported.

Blood pressure measurement. Two studies investigated the effect of case-mix correction on this process indicator.^20,21 Therefore, all factors were rarely (n ≤2) studied. No data on ranking or explained variance of case-mix adjustment models were reported.

Overview of the Results

Twenty-three case-mix factors were identified; the most frequently considered factors were demographics (especially age and gender), diabetes duration, and comorbidity, and there was a predominant focus on outcome indicators (A1C in particular). The rationale for selection of case-mix factors in adjustment models was reported in 6 studies (42%): Face validity and feasibility of measurement were most commonly noted. There is evidence that marital status and BMI do affect outcome indicator scores for A1C. For the other case-mix factors, associations with indicators were inconsistent, absent, or marginally studied. Six studies showed impact on ranking,^{5,6,7,15,11,21} while 5 did not^8,9,16,20,21 (2 of the 13 studies are not included here as ranking was not always studied).

DISCUSSION

To the best of our knowledge, this is the first systematic review aimed at identifying relevant case-mix factors for the 6 most commonly used quality indicators for diabetes care, which are outcome and process indicators for A1C, LDL-C, and blood pressure.

We found a consistent impact—ie, at least 3 studies reporting impact and no studies reporting otherwise—of marital status^5-7 and BMI^5,7,15 on the outcome indicator A1C. The demographic factor of marital status probably relates to a more regular lifestyle or getting support from a partner for self-management tasks, both resulting in better A1C values^22,23 as an outcome indicator. Also BMI, a diabetes-related factor, appeared to be consistently associated with this indicator.^5,7,15 Despite evidence of the association of BMI with hypertension and dyslipidemia,²⁴ the relevance of this factor was rarely studied for the other diabetes indicators.

Inconsistent impact was found for age (on outcome and process indicators for A1C, outcome indicator LDL-C), gender (outcome indicators A1C and LDL-C), ethnicity, education, diabetes duration, health status (outcome indicator A1C), and comorbidity (on outcome and process indicators for A1C and LDL-C). Age and gender were among the factors studied most often, but their relevance was not clearly shown. It is worth highlighting that ethnicity has been considered a case-mix factor for outcome^7,11,21 and process indicators,^7,21 although its impact appeared inconsistent. Inclusion of this factor in case-mix adjustment models is controversial, as it suggests that ethnicity causes differences in outcome of healthcare which are beyond the control of the healthcare provider.^8,25 Indeed, differences in blood-pressure levels exist across ethnicities; for example, black Africans generally have higher blood pressure, which is usually more difficult to treat,²⁶ favoring case-mix adjustment on these grounds. However, it cannot be ruled out that ethnic minorities, due to prejudices, language barriers, or other factors, receive suboptimal medical care. Correction for ethnicity then masks poor quality of care.

The inconsistent results for diabetes duration may be explained by different measurement methods.^{5,9,11,19,20,21} One could question the relevance of diabetes-related factors, such as creatinine clearance, when case-mix adjustment is used for profiling reasons, as kidney function is subject to many influences.²⁷ Although nephropathy is directly associated with diabetes management, the relative contribution of this specific variable to the quality of diabetes care needs more research. The inconsistent results for comorbidity regarding A1C indicators might be explained by variability in operationalization. While some studies used the Charlson comorbidity index,⁸ others used the US National Committee for Quality Assurance’s definition¹⁷ or a list of comorbidity conditions,¹⁶ which hampers direct comparison.

Finally, it is worth noting that for the case-mix factors of diabetes duration (for the outcome indicators for LDL-C and blood pressure), gender (outcome indicator blood pressure and process indicator A1C), and age and education (outcome indicator blood pressure) no impact was found in at least 3 studies. Researchers and quality assessors may consider omitting these variables from their specific case-mix adjustment models, which will be beneficial for the statistical power of their analysis. Also, stakeholders or users of quality information should be aware of the value of case-mix and case-mix factors, that case-mix models may vary across different quality indicators, and that it may not be self-evident that background characteristics such as age and gender need to be included.

Because the case-mix adjustment models varied widely, it is hard to draw any conclusions on the final impact of ranking. Researchers are encouraged to further explore this issue.

Limitations

The review was subject to some limitations. The included studies were cross-sectional, which makes it impossible to address causality. We judged the impact of a case-mix factor as robust when at least 3 studies showed the same (statistically significant) impact, or no impact, without other studies showing opposite results. This cut-off was arbitrarily set and we realize that robustness may require more studies showing similar results. In addition, in the case of 1 or more studies showing opposite results, this was classified as inconsistent results. Some of these inconsistent results could possibly be attributed to the variety in the study populations included—that is, primary versus secondary care, which may be related to the severity of the diabetes and might have affected the impact of case-mix factors. Again, the number of studies included in this review is too small to draw firm conclusions. Finally, in reviewing the scientific literature, we did not seek to limit the relevance of the results for research aims, but we did seek to expand the relevance, when possible, for use in real-world quality assessment and benchmarking.

CONCLUSIONS

Despite the compelling call for public transparency on quality of healthcare—for which case-mix adjustment on quality indicators is generally considered essential for fair comparison among healthcare providers—there still are large interstices in this field of research, as was shown by the current review. There appears to be a tendency for inclusion of factors based on face validity, often without a clear rationale, and scientific evidence on relevance and impact of these factors is still limited. From the available studies, we only found evidence for 2 factors—marital status and BMI—to be used in case-mix models for the outcome indicator A1C. Simple advice to the developers of diabetes quality indicators is therefore not possible. Author Affiliations: Radboud University Medical Center, Radboud Institute for Health Sciences, Scientific Institute for Quality of Healthcare (IQ healthcare) (HC, GEV, JCCB), Nijmegen, The Netherlands; Rijnstate Hospital (JGMM), Arnhem, The Netherlands.

Source of Funding: None.

Author Disclosures: The authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Authorship Information: Concept and design (HC, JGMM, GEV, JCCB); acquisition of data (HC, GEV); analysis and interpretation of data (HC, JGMM, GEV, JCCB); drafting of the manuscript (HC, JGMM, GEV, JCCB); critical revision of the manuscript for important intellectual content (HC, JGMM, JCCB); provision of patients or study materials (JGMM); administrative, technical, or logistic support (JGMM); and supervision (JCCB). Note: HC and JGMM share first authorship.

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