OBJECTIVETo examine the longitudinal effects of medication nonadherence (MNA) on key costs and estimate potential savings from increased adherence using a novel methodology that accounts for shared correlation among cost categories.RESEARCH DESIGN AND METHODSVeterans with type 2 diabetes (740,195) were followed from January 2002 until death, loss to follow-up, or December 2006. A novel multivariate, generalized, linear, mixed modeling approach was used to assess the differential effect of MNA, defined as medication possession ratio (MPR) ≥0.8 on healthcare costs. A sensitivity analysis was performed to assess potential cost savings at different MNA levels using the Consumer Price Index to adjust estimates to 2012 dollar value.RESULTSMean MPR for the full sample over 5 years was 0.78, with a mean of 0.93 for the adherent group and 0.58 for the MNA group. In fully adjusted models, all annual cost categories increased ∼3% per year (P = 0.001) during the 5-year study time period. MNA was associated with a 37% lower pharmacy cost, 7% lower outpatient cost, and 41% higher inpatient cost. Based on sensitivity analyses, improving adherence in the MNA group would result in annual estimated cost savings ranging from ∼$661 million (MPR <0.6 vs. ≥0.6) to ∼$1.16 billion (MPR <1 vs. 1). Maximal incremental annual savings would occur by raising MPR from <0.8 to ≥0.8 ($204,530,778) among MNA subjects.CONCLUSIONSAggressive strategies and policies are needed to achieve optimal medication adherence in diabetes. Such approaches may further the so-called “triple aim” of achieving better health, better quality care, and lower cost.

Objective. To investigate potential bias in the use of the conventional linear instrumental variables (IV) method for the estimation of causal effects in inherently nonlinear regression settings. Data Sources. Smoking Supplement to the 1979 National Health Interview Survey, National Longitudinal Alcohol Epidemiologic Survey, and simulated data. Study Design. Potential bias from the use of the linear IV method in nonlinear models is assessed via simulation studies and real world data analyses in two commonly encountered regression setting: (1) models with a nonnegative outcome (e.g., a count) and a continuous endogenous regressor; and (2) models with a binary outcome and a binary endogenous regressor. Principle Findings. The simulation analyses show that substantial bias in the estimation of causal effects can result from applying the conventional IV method in inherently nonlinear regression settings. Moreover, the bias is not attenuated as the sample size increases. This point is further illustrated in the survey data analyses in which IV-based estimates of the relevant causal effects diverge substantially from those obtained with appropriate nonlinear estimation methods. Conclusions. We offer this research as a cautionary note to those who would opt for the use of linear specifications in inherently nonlinear settings involving endogeneity.Key Words. Econometrics, nonlinear models, health economics When analyzing data with the goal of informing health policy, the ability to draw true causal inference from the estimation results is of paramount importance. The typical health policy analysis focuses on identifying the effect that a variable, over which there exists some degree of policy control (x p --henceforth the policy variable), has on an outcome of some policy interest (y--the outcome variable). Empirical analyses that offer estimates of mere associations between x p and y are of little value to policy makers. Estimating the desired causal effect of x p on y is not straightforward and is particularly r Health Research and Educational Trust DOI: 10.1111/j. 1475-6773.2007.00807.x 1102 difficult in the context of nonexperimental (survey) data. In observational surveys respondent behavior, as manifested in the value of y, can be influenced by a myriad of stimuli aside from the policy variable x p . Such alternative influences on y will obfuscate causal inference if they are also correlated with x p . If not properly taken into account, this will lead to bias in conventional estimation of causal effects. 1 For example, Mullahy (1997) posits that an individual's habit stock, accumulated over previous periods of smoking, will have an effect on current cigarette demand. 2 We might observe, for instance, that individuals with higher habit stocks have greater demands for cigarettes. Interpreting such an observation, as indicative of the causal effect of habit stock on current cigarette smoking will, however, be upward biased if ''healthminded'' individuals tend to smoke less both currently and in the past.Some co...

With the development of a measure of serious psychological distress (SPD) in 2002, more attention is being paid to the association of SPD with diabetes outcomes and processes of care. We review the literature on the relationship between SPD and diabetes processes of care and outcomes, as well as the literature on the relationship between specific mental health diagnoses and diabetes processes of care and outcomes during the 2010 to 2011 period. There is an extensive literature on the association of mental health diagnoses with diabetes outcomes, especially for depression. Because the Kessler scale measures a much broader range of mental health issues than any specific DSM-IV/Structured Clinical Interview for DSM Disorders diagnosis and is designed to assess SPD at the population level, additional research needs to be conducted both in clinical settings and using large administrative datasets to examine the association between SPD and diabetes outcomes and processes of care.

Posttraumatic stress disorder (PTSD) is associated with functional impairment, co-occurring diagnoses, and increased health care utilization. Associated high demand for health care services is an important contributor to the large public-health cost of PTSD. Treatments incorporating exposure therapy are efficacious in ameliorating or eliminating PTSD symptoms. Accordingly, the Veterans Health Administration has made significant investments toward nationwide dissemination of a manualized exposure therapy protocol, prolonged exposure (PE). PE is effective with veterans; however, the relationship between PE and mental health service utilization is unknown. The current study investigates PE as it relates to actual tracked mental health service utilization in an urban VA medical center. A sample of 60 veterans with a diagnosis of PTSD was used to examine mental health service utilization in the 12-months prior to and 12-months after being offered PE. Hierarchical Linear Models and traditional repeated-measures ANOVA were used to estimate R²- and d-type effect sizes for service utilization. Associated estimated cost saving are reported. PE was associated with large reductions in symptoms and diagnosis remission. Treatment was also associated with statistically significant, large reductions in mental health service utilization for veterans who completed treatment. Findings suggest that expanding access to PE can increase access to mental health services in general by decreasing ongoing demand for specialty care clinical services.