Publications

BACKGROUND: Health insurance claims data may play an important role for health care systems and payers in monitoring the nonmedical use of prescription opioids (NMPO) among patients. However, these systems require valid methods for identifying NMPO if they are to target individuals for intervention. Limited efforts have been made to define NMPO using administrative data available to health systems and payers. We conducted a systematic review of publications that defined and measured NMPO within health insurance claims databases in order to describe definitions of NMPO and identify areas for improvement.

METHODS: We searched 8 electronic databases for articles that included terms related to NMPO and health insurance claims. A total of 2613 articles were identified in our search. Titles, abstracts, and article full texts were assessed according to predetermined inclusion/exclusion criteria. Following article selection, we extracted general information, conceptual and operational definitions of NMPO, methods used to validate operational definitions of NMPO, and rates of NMPO.

RESULTS: A total of 7 studies met all inclusion criteria. A range of conceptual NMPO definitions emerged, from concrete concepts of abuse to qualified definitions of probable misuse. Operational definitions also varied, ranging from variables that rely on diagnostic codes to those that rely on opioid dosage and/or filling patterns. Quantitative validation of NMPO definitions was reported in 3 studies (e.g., receiver operating curves or logistic regression), with each study indicating adequate validity. Three studies reported qualitative validation, using face and content validity. One study reported no validation efforts. Rates of NMPO among the studies' populations ranged from 0.75% to 10.32%.

CONCLUSIONS: Disparate definitions of NMPO emerged from the literature, with little uniformity in conceptualization and operationalization. Validation approaches were also limited, and rates of NMPO varied across studies. Future research should prospectively test and validate a construct of NMPO to disseminate to payers and health officials.

Effective medications are a cornerstone of prevention and disease treatment, yet only about half of patients take their medications as prescribed, resulting in a common and costly public health challenge for the US health care system. Since poor medication adherence is a complex problem with many contributing causes, there is no one universal solution. This paper describes interventions that were not only effective in improving medication adherence among patients with diabetes, but were also potentially scalable (ie, easy to implement to a large population). We identify key characteristics that make these interventions effective and scalable. This information is intended to inform health care systems seeking proven, low resource, cost-effective solutions to improve medication adherence.

BACKGROUND: Nursing home patients with dementia may be more likely to suffer adverse drug events from suboptimal prescribing. Previous studies have not used national samples, nor have they examined multiple types of suboptimal prescribing by dementia severity.

OBJECTIVE: To examine the prevalence of and factors associated with potentially suboptimal prescribing in older veteran nursing home patients with dementia.

METHODS: This is a retrospective descriptive study of 1303 veterans 65 years or older admitted between January 1, 2004, and June 30, 2005, with dementia for long stays (90+ days) to 133 Veterans Affairs Community Living Centers. Dementia severity was determined by the Cognitive Performance Scale and functional status dependences.

RESULTS: Overall, 70.2% with mild-moderate dementia (n = 1076) had underuse because they did not receive an acetylcholinesterase inhibitor (AChEI), and 27.2% had evidence of inappropriate use because of a drug-disease or drug-drug-disease interaction. Of the 227 with severe dementia, 36.1% had overuse by receiving an AChEI or lipid-lowering or other agents, and 25.1% had evidence of inappropriate use as a result of a drug-disease or drug-drug interaction. Multinomial logistic regression analyses among those with mild to moderate dementia identified that living in the South versus other regions was the single factor associated with all 3 types of suboptimal prescribing. In those with severe dementia, antipsychotic use was associated with all 3 suboptimal prescribing types.

CONCLUSIONS: Potentially suboptimal prescribing was common in older veteran nursing home patients with dementia. Clinicians should develop a heightened awareness of these problems. Future studies should examine associations between potentially suboptimal prescribing and health outcomes in patients with dementia.

BACKGROUND: Long-acting insulin analogues (eg, insulin glargine and insulin detemir) are an alternative to neutral protamine Hagedorn (NPH) insulin for maintaining glycemic control in patients with diabetes. Clinical trials comparing analogue insulin and NPH have neither been adequately powered nor had sufficient follow-up to examine long-term health outcomes.

OBJECTIVES: To compare the effects of NPH and long-acting insulin analogues on long-term outcomes.

STUDY DESIGN: This retrospective observational study relied on administrative data from the Veterans Health Administration and Medicare from 2000 to 2010. Local variations in analogue insulin prescribing rates were used in instrumental variable models to control for confounding. Outcomes were assessed using survival models.

METHODS: The study population included US veterans dually enrolled in Medicare who received at least 1 prescription for oral diabetes medication and then initiated long-acting insulin between 2001 and 2009. Outcomes included ambulatory care-sensitive condition (ACSC) hospitalizations and mortality.

RESULTS: There was no significant relationship between type of insulin and ACSC hospitalization or mortality. The hazard ratio for mortality of individuals starting a long-acting analogue insulin was 0.97 (95% CI, 0.85-1.11), and was 1.05 (95% CI, 0.95-1.16) for ACSC hospitalization. Differences in risk remained insignificant when predicting diabetes-specific ACSC hospitalizations, but starting on long-acting analogue insulin significantly increased the risk of a cardiovascular-specific ACSC hospitalization.

CONCLUSIONS: We found no consistent difference in long-term health outcomes when comparing use of long-acting insulin analogues and NPH insulin. The higher cost of analogue insulin without demonstrable clinical benefit raises questions of its cost-effectiveness in the treatment of patients with diabetes.

OBJECTIVES: Use of buprenorphine - an effective treatment for opioid use disorders (OUDs) - has increased rapidly in recent years and is often financed by Medicaid. We investigated predictors of buprenorphine treatment, patterns of care, and quality of care in a large state Medicaid program.

METHODS: Data from Pennsylvania Medicaid from 2007 to 2012 provided information regarding diagnoses, demographic characteristics, enrollment, and use of inpatient and outpatient services, and prescription drugs. We identified adult enrollees using buprenorphine, and examined prevalence of OUD diagnosis and patterns of use (duration and dose) and quality of care (physician visits, receipt of behavioral health counseling, urine drug screens, and other prescription drug use). We use a mixed logistic regression model to examine enrollee characteristics associated with buprenorphine use.

RESULTS: The share of enrollees with OUD filling prescriptions for buprenorphine increased from 2985 (9.8%) to 12,691 (25.2%) from 2007 to 2012. Between 26.2 and 32.0% of enrollees using buprenorphine had no diagnosis of OUD, depending on the year. Only 60.1% of enrollees with buprenorphine use received at least one urine drug screen, 41.0% had behavioral health counseling services, and 34.7 and 38.0% had other opioid and benzodiazepine claims, respectively, concomitant with buprenorphine use. Quality of care was lower among those with no OUD diagnosis recorded. The mean daily doses of buprenorphine decreased over time. We found wide variation in likelihood of buprenorphine use among those with OUD based upon age, sex, and race.

CONCLUSIONS: Increases in buprenorphine treatment in a Medicaid population were observed across time; however, increases varied by age, sex, and rate, and the quality of care received seemed to be generally poor. The quality of the provision of buprenorphine treatment occurring in Medicaid populations should be further explored.

Little is known about the health status of the 7.3 million Americans who enrolled in insurance plans through the Marketplaces established by the Affordable Care Act in 2014. Medication use may provide an early indicator of the health needs and access to care among Marketplace enrollees. We used data from January-September 2014 on more than one million Marketplace enrollees from Express Scripts, the largest pharmacy benefit management company in the United States. We compared the characteristics and medication use between early and late Marketplace enrollees and between all Marketplace enrollees and enrollees with employer-sponsored insurance. Among Marketplace enrollees, we found that those who enrolled earlier (October 2013-February 2014) were older and used more medication than later enrollees. Marketplace enrollees, as a whole, had lower average drug spending and were less likely to use most medication classes than the employer-sponsored comparison group. However, Marketplace enrollees were more likely to use medicines for hepatitis C and particularly for HIV.

BACKGROUND: Quality improvement efforts are frequently tied to patients achieving ≥80% medication adherence. However, there is little empirical evidence that this threshold optimally predicts important health outcomes.

OBJECTIVE: To apply machine learning to examine how adherence to oral hypoglycemic medications is associated with avoidance of hospitalizations, and to identify adherence thresholds for optimal discrimination of hospitalization risk.

METHODS: A retrospective cohort study of 33,130 non-dual-eligible Medicaid enrollees with type 2 diabetes. We randomly selected 90% of the cohort (training sample) to develop the prediction algorithm and used the remaining (testing sample) for validation. We applied random survival forests to identify predictors for hospitalization and fit survival trees to empirically derive adherence thresholds that best discriminate hospitalization risk, using the proportion of days covered (PDC).

OUTCOMES: Time to first all-cause and diabetes-related hospitalization.

RESULTS: The training and testing samples had similar characteristics (mean age, 48 y; 67% female; mean PDC=0.65). We identified 8 important predictors of all-cause hospitalizations (rank in order): prior hospitalizations/emergency department visit, number of prescriptions, diabetes complications, insulin use, PDC, number of prescribers, Elixhauser index, and eligibility category. The adherence thresholds most discriminating for risk of all-cause hospitalization varied from 46% to 94% according to patient health and medication complexity. PDC was not predictive of hospitalizations in the healthiest or most complex patient subgroups.

CONCLUSIONS: Adherence thresholds most discriminating of hospitalization risk were not uniformly 80%. Machine-learning approaches may be valuable to identify appropriate patient-specific adherence thresholds for measuring quality of care and targeting nonadherent patients for intervention.

IMPORTANCE: Self-monitoring of blood glucose is a costly component of care for diabetes mellitus, with unclear benefits for patients not taking insulin. Veterans with dual Department of Veterans Affairs (VA) and Medicare benefits have access to test strips through both systems, raising the potential for overuse.

OBJECTIVES: To examine the patterns of test strip receipt among older veterans with diabetes and determine whether receipt of strips from dual health care systems is associated with overuse.

DESIGN, SETTING, AND PARTICIPANTS: We performed a cross-sectional, retrospective cohort study using national VA administrative data linked to Medicare Parts A, B, and D claims for fiscal years 2008 and 2009. A total of 363,996 community-dwelling veterans 65 years or older with diabetes who used the VA health care system and received test strips in fiscal year 2009 were included in the study.

EXPOSURES: Receipt of test strips from the VA only, Medicare only, or both the VA and Medicare; covariates included sociodemographics, comorbidity, diabetes complications, and hemoglobin A1c level.

MAIN OUTCOMES AND MEASURES: Quantity of test strips dispensed and overuse of test strips, defined as more than 1 strip per day (>365 strips per year) among those taking no diabetes medications, oral diabetes medications alone, or long-acting insulin without short-acting insulin or more than 4 strips per day (>1460 strips per year) among those taking short-acting insulin.

RESULTS: Overall, 260,688 older veterans (71.6%) with diabetes received strips from the VA only, 82,826 (22.8%) from Medicare only, and 20,482 (5.6%) from the VA and Medicare. Veterans receiving strips from both the VA and Medicare received more strips (median, 600; interquartile range [IQR], 350-1000) than the Medicare only (median, 400; IQR, 200-700) and VA only (median, 200; IQR, 100-500) groups (P < .001) and had substantially greater odds of overuse than the VA only group (55.4% vs 15.8%) (adjusted odds ratio [OR], 16.3; 95% CI, 14.6-18.1 for no medications; 55.3% vs 6.0%; OR, 19.8; 95% CI, 18.9-20.8 for oral medications; 87.4% vs 65.5%; OR, 3.69; 95% CI, 3.30-4.14 for long-acting insulin; and 32.8% vs 13.5%; OR, 3.24; 95% CI, 3.05-3.45 for short-acting insulin). Patterns were similar when using more conservative thresholds of overuse.

CONCLUSIONS AND RELEVANCE: Veterans who receive glucose test strips through both the VA and Medicare use more strips and are more likely to potentially overuse strips. These results illustrate the profound importance of understanding dual VA and Medicare coverage and are emblematic of waste and inefficiency.