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The American Journal of Managed Care January 2014
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Process of Care Compliance Is Associated With Fewer Diabetes Complications
Felicia J. Bayer, PhD; Deron Galusha, MS; Martin Slade, MPH; Isabella M. Chu, MPH; Oyebode Taiwo, MBBS, MPH; and Mark R. Cullen, MD
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Process of Care Compliance Is Associated With Fewer Diabetes Complications

Felicia J. Bayer, PhD; Deron Galusha, MS; Martin Slade, MPH; Isabella M. Chu, MPH; Oyebode Taiwo, MBBS, MPH; and Mark R. Cullen, MD
Adherence to process of care measures was associated with reduced risk of 2 diabetes complications or any of 4 complications in a national industrial cohort.
Characteristics of the baseline sample are shown in Table 1; the same subjects stratified by treatment status are shown in Table 2. At baseline, the group receiving all 3 process measures was smaller (n = 267) than the comparison group  (n = 1530). Differences between the 2 groups were assessed with 2-tailed t tests for independent samples. Despite the sample size differences, there were no statistically significant differences between the 2 groups during the baseline period (2003) for demographic, socioeconomic status, or modifiable risk variables. However, there was evidence that the group receiving all 3 process measures was sicker at baseline. They were significantly more likely to be receiving insulin than the comparison group (27.7% vs 18.8%; P = .0007) and were also more likely to fall into the higher quartiles of health severity risk score (P = .043).

Table 3 shows the characteristics of those who were censored compared with those who remained in employment throughout the   follow-up period. Although workers who left were older and less well paid on average at baseline, differences between censored employees and non-censored employees were not significant for race, marital status, occupational group (salary or hourly), insulin use, health severity risk score, and importantly, likelihood of receiving the treatment.

In total, 366 persons with diabetes (20%, n = 1797) had medical claims for at least 1 of the 4 complications with a mean time to complication of 29.1 months. The most frequent complication in the cohort was CAD (16.9%) with a mean time to complication of 26.6 months, followed by stroke (8.7%, 33.1 months), HF (5.8%, 29.7 months), and RD (4.9%, 38.1 months). Those getting all 3 process measures of care fared better for all end points. Hazard ratios for 2 of the 4 complications were significant: HF (HR = 0.39, 95% confidence interval [CI], 0.19-0.81; P = .012) and RD (HR = 0.48, 95% CI, 0.24-0.95; P = .034). The HRs for CAD (HR = 0.70, 95% CI, 0.49-1.02; P = .064) and stroke (HR = 0.63, 95% CI, 0.38-1.07; P = .089) showed the same trend but were not significant. Hazard ratios with CIs for each and any of the  4 end points are summarized in Table 4.

The HR for submitting a medical claim for any of the 4 complications was significantly lower for those receiving all 3 process measures (HR = 0.66, 95% CI, 0.48-0.91; P = .01). Covariates associated with increased risk included increasing age 46-51 years (HR = 1.88, 95% CI, 1.36-2.61; P = .0001), 52-56 years (HR = 2.06, 95% CI, 1.47-2.89; P <.0001), and 57-64 years (HR = 3.09, 95% CI, 2.15-4.46; P <.0001); health severity  risk scores of 2.1 or higher (HR = 1.91, 95% CI, 1.36-2.68; P = .0002); and smoking (HR = 1.44, 95% CI, 1.01-2.07; P = .047).  Differences in all other covariates were not statistically significant. The Kaplan-Meier estimates of cumulative hazards for all end points are depicted in Figures 1 through 5.

We conducted 2 sensitivity analyses. The first used 2 years (2003 and 2004) of continuous process of care measures to reduce the likelihood of random misclassification. Results showed the same trends and similar point estimates (data not shown), although with shorter follow-up (2005-2009) these results were no longer significant. Likewise we tested for robustness within strata by sex, hourly versus salaried job status, and initial insulin use. Results revealed comparable point estimates for all strata with the exception of the women, in whom HR estimates for each complication hovered around 1. In general, the point estimates within strata were similar to those found for the full cohort, but with wider CIs.

DISCUSSION

This study compared 2 groups of employees with diabetes to assess how differences in process measures of care might have affected the onset of complications associated with diabetes. Significant differences in time to complication were observed between the 2 groups for 2 of the 4 complications  (HF and RD) and for any of the 4 complications. Remarkably, those employees getting optimal process scores were, on average, sicker at the outset based on greater insulin use and increased risk severity scores; hence, they would have been expected a priori to have worse outcomes.

Many studies have assessed whether interventions at the provider level improve processes of care and intermediate outcomes, but the effect of such process improvements on complications remains less clear because the outcomes themselves are rarely assessed.38 This study estimates the impact of process measures of quality care by analyzing data in a national sample across multiple physician groups administering diabetes care within the same health insurance plan structure. This study is the first to our knowledge confirming, in practice, the expectations for benefit from compliance with recommended process measures of care in which follow-up was long enough to observe the major clinical end points of interest.

That said, there remain significant limitations to our methods and our ability to draw causal inference from the results. We relied on claims data for recognition of our population with diabetes, for ascertainment of complications, and for assessment of the critical covariate of interest—execution of all of the quality of care diagnostic measures in the baseline year. Each step entails opportunity for  misclassification. Although the diagnosis of diabetes is readily inferred from claims with high sensitivity and specificity,39 comparable data comparing claims with verifiable sources such as medical records or examinations do not exist for any of the major complications with the exception of RD,40 which has been found to be identified with high specificity but less sensitivity than diabetes. That would not, however, bias our findings for patients who had been identified as having RD. Thus, it is possible that the end points misclassified cohort members in both directions and that some with complications already evident were not appropriately excluded. The impact of such misclassifications, assuming they were random, would likely drive our result toward the null, but there is no assurance that such errors were random.

Another concern is the impact on our conclusions of unobserved variables (eg, body mass index, duration of diabetes) as noted above. Although there is no strong prior reason to suspect that such variables were distributed in such a way as to confound our  result, more obese subjects might have received consistently poorer care and, independently, had a higher risk of bad outcomes. The impact of censorship may also have resulted in possible bias, as those faring most poorly might be expected to leave prematurely more often. It is reassuring that censorship was not associated with baseline characteristics of disease severity nor  with treatment group, but the size of loss—more than half of the cohort—introduces concern for unmeasured bias.

The employees in this study may not represent a generalizable population, as they work for a stable employer with rich and uniform benefits in the heavy manufacturing sector, both less common features than a generation ago. Our study panel requirements demanded a group stably employed during a period of years, further rendering it less representative of the larger workforce.

Perhaps most difficult to fully address is the possibility of endogenous differences among persons with diabetes leading some to both seek better care for their disease—picking better qualified doctors and/or advising them what tests to order—and take better are of themselves in other ways, collectively resulting in better outcomes. Nonetheless, it is noteworthy that in the primary test of  the hypothesis, the baseline characteristics created an uneven playing field, with more ostensibly sick patients having a higher rate of better care; thus, the deck was stacked against those with better care having better outcomes, an effect that began to be visually apparent in the third observation year (see Figure 5). It is also reassuring that our result survived the various sensitivity tests. We are exploring instrumental variables and other approaches to examine the causal pathway further, including assessment of the relationships between the process measures and intermediate outcomes such as medication change, adherence, frequency of exams, and the like.

CONCLUSION

Limitations notwithstanding, these results provide further support for process measures and efforts to promote their application in  practice, starting with the obvious; the proportion of employees with diabetes getting the standard of care based on systematic  reviews1,38 was disturbingly low (just under 15%) in our fully insured population. If even a modest portion of the association with outcome is causal, there is a considerable opportunity for benefit from driving better processes of care.

Author Affiliations: From Alcoa Aluminum (FJB), Pittsburgh, PA; Yale Occupational & Environmental Medicine (DG, MS, OT), Yale School of Medicine, New Haven, CT; Stanford University School of Medicine (IMC, MRC), General Medical Disciplines, Stanford, CA.

Author Disclosures: Dr Bayer reports employment with Alcoa Aluminum, who provided data for this study. The other 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 (FJB, DG, MS, OT, MRC); acquisition of data (FJB, MRC); analysis and interpretation of data (FJB, DG, MS, IMC, OT, MRC); drafting of the manuscript (FJB, IMC, MRC); critical revision of the manuscript for important intellectual content (FJB, DG, IMC, OT, MRC); statistical analysis (FJB, DG, MS, MRC); provision of study materials or patients (FJB); obtaining funding (FJB, MRC); administrative, technical, or logistic support (FJB, IMC); and supervision (MRC).

Financial Source: This study was supported by National Institutes for Health grant 5RO1AG026291-04 and continuing support from Alcoa Aluminum. The funders had no role in the design of this study; collection, management, analysis, and interpretation of the data; conduct of this study; or preparation or approval of the manuscript. Alcoa Aluminum reviewed the manuscript prior to publication.

Address correspondence to: Mark R. Cullen, MD, Stanford University School of Medicine, General Medical Disciplines, 1265 Welch Rd, MSOB X-338, Stanford, CA 94305. E-mail: mrcullen@stanford.edu.
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