Residual Confounding in Health Plan Performance Assessments: Evidence From Randomization in Medicaid.

BACKGROUND: Risk adjustment is used widely in payment systems and performance assessments, but the extent to which it distinguishes plan or provider effects from confounding due to patient differences is typically unknown. OBJECTIVE: To assess the degree to which risk-adjusted measures of health plan performance adequately adjust for the variation across plans that arises because of differences in patient characteristics (residual confounding). DESIGN: Comparison between plan performance estimates based on enrollees who made plan choices (observational population) and estimates based on enrollees assigned to plans (randomized population). SETTING: Natural experiment in which more than two thirds of a state's Medicaid population in 1 region was randomly assigned to 1 of 5 plans. PARTICIPANTS: 137 933 enrollees in 2013 to 2014, of whom 31.1% selected a plan and 68.9% were randomly assigned to 1 of the same 5 plans. MEASUREMENTS: Annual total spending (that is, payments to providers), primary care use, dental care use, and avoidable emergency department visits, all scored as plan-specific deviations from the "average" plan performance within each population. RESULTS: Enrollee characteristics were appreciably imbalanced across plans in the observational population, as expected, but were not in the randomized population. Annual total spending varied across plans more in the observational population (SD, $147 per enrollee) than in the randomized population (SD, $70 per enrollee) after accounting for baseline differences in the observational and randomized populations and for differences across plans. On average, a plan's spending score (its deviation from the "average" performance) in the observational population differed from its score in the randomized population by $67 per enrollee in absolute value (95% CI, $38 to $123), or 4.2% of mean spending per enrollee (P = 0.009, rejecting the null hypothesis that this difference would be expected from sampling error). The difference was reduced modestly by risk adjustment to $62 per enrollee (P = 0.012). Residual confounding was similarly substantial for most other performance measures. Further adjustment for social factors did not materially change estimates. LIMITATION: Potential heterogeneity in plan effects between the 2 populations. CONCLUSION: Residual confounding in risk-adjusted performance assessments can be substantial and should caution policymakers against assuming that risk adjustment isolates real differences in plan performance. PRIMARY FUNDING SOURCE: Arnold Ventures.

A murine model of tuberculosis/type 2 diabetes comorbidity for investigating the microbiome, metabolome and associated immune parameters.

Tuberculosis (TB) is one of the deadliest infectious diseases in the world. The metabolic disease type 2 diabetes (T2D) significantly increases the risk of developing active TB. Effective new TB vaccine candidates and novel therapeutic interventions are required to meet the challenges of global TB eradication. Recent evidence suggests that the microbiota plays a significant role in how the host responds to infection, injury and neoplastic changes. Animal models that closely reflect human physiology are crucial in assessing new treatments and to decipher the underlying immunological defects responsible for increased TB susceptibility in comorbid patients. In this study, using a diet-induced murine T2D model that reflects the etiopathogenesis of clinical T2D and increased TB susceptibility, we investigated how the intestinal microbiota may impact the development of T2D, and how the gut microbial composition changes following a very low-dose aerosol infection with Mycobacterium tuberculosis (Mtb). Our data revealed a substantial intestinal microbiota dysbiosis in T2D mice compared to non-diabetic animals. The observed differences were comparable to previous clinical reports in TB patients, in which it was shown that Mtb infection causes rapid loss of microbial diversity. Furthermore, diversity index and principle component analyses demonstrated distinct clustering of Mtb-infected non-diabetic mice vs. Mtb-infected T2D mice. Our findings support a broad applicability of T2D mice as a tractable small animal model for studying distinct immune parameters, microbiota and the immune-metabolome of TB/T2D comorbidity. This model may also enable answers to be found to critical outstanding questions about targeted interventions of the gut microbiota and the gut-lung axis.