Hidden Complexities in Assessment of Glycemic Outcomes

Abstract

OBJECTIVETo evaluate facility rankings in achieving <7% A1C levels based on the complexity of glycemic treatment regimens using threshold and continuous measures.RESEARCH DESIGN AND METHODSWe conducted a retrospective administrative data analysis of Veterans Health Administration Medical Centers in 2003–2004. Eligible patients were identified using National Committee for Quality Assurance (NCQA) measure specifications. A complex glycemic regimen (CGR) was defined as receipt of insulin or three oral agents. Facilities were ranked using five ordinal categories based up both z score distribution and statistical significance (P < 0.05). Rankings using the NCQA definition were compared with a subset receiving CGRs using both a <7% threshold and a continuous measure awarding proportional credit for values between 7.9 and <7.0%. Ranking correlation was assessed using the Spearman correlation coefficient.RESULTSA total of 203,302 patients (mean age 55.2 years) were identified from 127 facilities (range 480–5,411, mean 1,601); 26.7% (17.9–35.2%) were receiving CGRs, including 22.0% receiving insulin. Mean A1C and percent achieving A1C <7% were 7.48 and 48% overall and 8.32 and 24.8% for those receiving CGRs using the threshold measure; proportion achieved was 60.1 and 37.2%, respectively, using the continuous measure. Rank correlation between the overall and CGR subset was 0.61; 8 of 24 of the highest or lowest ranked facilities changed to nonsignificance status; an additional five sites changed rankings.CONCLUSIONSFacility rankings in achieving the NCQA <7% measure as specified differ markedly from rankings using the CGR subset. Measurement for public reporting or payment should stratify rankings by CGR. A continuous measure may better align incentives with treatment intensity.

The role of matching when adjusting for baseline differences in the outcome variable of comparative effectiveness studies

“…For instance, even when measured at accredited laboratories, the true value of a single HbA1c measurement of 7% could vary between 6.65 and 7.35%, indicating a permissible intraassay coefficient of variation of 5% [9,10]. The implications of this variability on the selection of antidiabetic regimens based on single HbA1c values and the definition of glycemic control targets have been previously discussed [10]. In our study, the influence of baseline imbalances was more modest in simulations where no or little measurement error was allowed.…”

“…The data simulations encompassed a range of hypothetical true treatment effects ranging from −1 to +1%, in 0.25% increments. As HbA1c measurements are known to have measurement error, this was induced in our simulations including no measurement error, small (as previously reported, coefficient of variation (CV = 5%) [9,10], and moderate measurement error (CV = 10%) without changing the total variance in the baseline and 12-month HbA1c (see R code in the Supplementary Material; see online at www.futuremedicine.com/doi/full/10.2217/CER.15.16) [9,10]. This yielded 100,000 simulated datasets per treatment effect × baseline difference × measurement error combination; 900,000 per baseline difference × measurement error combination when aggregating over the treatment effect levels.…”

Effects of Changes in Diabetes Pay‐for‐Performance Incentive Designs on Patient Risk Selection

“…P4P incentives could be designed much proactively to reward providers for including patients with multiple chronic conditions and encourage diabetes care teams to include patients who might require more effort and time to achieve targeted outcomes (Pogach et al. ; Chang, Lin, and Aron ). P4P incentives can also be designed to promote provider integration between both primary care physician and specialists to improve the care of diabetic patients (Mechanic and Altman ).…”

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“…Evidence is accumulating that tight control may not in fact improve patient outcomes, and that harm to patients may actually increase with aggressive management of blood glucose and blood pressure. [24][25][26][27][28][29] An alternative hypothesis is that our data refl ect some degree of commercial infl uence over physician prescribing practices. A conceptual model, the inverse benefi t law, proposed by Brody and Light, 13 suggests how this might occur.…”