Empirically identified networks of healthcare providers for adults with mental illness

Background Policies target networks of providers who treat people with mental illnesses, but little is known about the empirical structures of these networks and related variation in patient care. The goal of this paper is to describe networks of providers who treat adults with mental illness in a multi-payer database based medical claims data in a U.S. state. Methods Provider networks were identified and characterized using paid inpatient, outpatient and pharmacy claims related to care for people with a mental health diagnosis from an all-payer claims dataset that covers both public and private payers. Results Three nested levels of network structures were identified: an overall network, which included 21% of providers (N = 8256) and 97% of patients (N = 476,802), five communities and 24 sub-communities. Sub-communities were characterized by size, provider composition, continuity-of-care (CoC), and network structure measures including mean number of connections per provider (degree) and average number of connections who were connected to each other (transitivity). Sub-community size was positively associated with number of connections (r = .37) and the proportion of psychiatrists (r = .41) and uncorrelated with network transitivity (r = −.02) and continuity of care (r = .00). Network transitivity was not associated with CoC after adjustment for provider type, number of patients, and average connection CoC (p = .85). Conclusions These exploratory analyses suggest that network analysis can provide information about the networks of providers that treat people with mental illness that is not captured in traditional measures and may be useful in designing, implementing, and studying interventions to improve systems of care. Though initial results are promising, additional empirical work is needed to develop network-based measures and tools for policymakers

Changes in Physician Antipsychotic Prescribing Preferences, 2002–2007

Objective Physician antipsychotic prescribing behavior may be influenced by comparative effectiveness evidence, regulatory warnings, and formulary and other restrictions on these drugs. This study measured changes in the degree to which physicians are able to customize treatment choices and changes in physician preferences for specific agents after these events. Methods The study used 2002–2007 prescribing data from the IMS Health Xponent database and data on physician characteristics from the American Medical Association for a longitudinal cohort of 7,399 physicians. Descriptive and multivariable regression analyses were conducted of the concentration of prescribing (physician-level Herfindahl index) and preferences for and likelihood of prescribing two first-generation antipsychotics and six second-generation antipsychotics. Analyses adjusted for prescribing volume, specialty, demographic characteristics, practice setting, and education. Results Antipsychotic prescribing was highly concentrated at the physician level, with a mean unadjusted Herfindahl index of .33 in 2002 and .29 in 2007. Psychiatrists reduced the concentration of their prescribing more over time than did other physicians. High-volume psychiatrists had a Herfindahl index that was half that of low-volume physicians in other specialties (.18 versus .36), a difference that remained significant (p<.001) after adjustment for physician characteristics. The share of physicians preferring olanzapine dropped from 29.9% in 2002 to 10.3% in 2007 (p<.001) while the share favoring quetiapine increased from 9.4% to 44.5% (p<.001). Few physicians (<5%) preferred a first-generation antipsychotic in 2002 or 2007. Conclusions Preferences for specific antipsychotics changed dramatically during this period. Although physician prescribing remained heavily concentrated, the concentration decreased over time, particularly among psychiatrists.National Institute of Mental Health (U.S.) (Grant R01MH093359)National Institute of Mental Health (U.S.) (Grant P30 MH090333)National Institute of Mental Health (U.S.) (Grant R01MH087488)Agency for Healthcare Research and Quality (Grant R01HS017695)Robert Wood Johnson Foundation (Investigator Award in Health Policy Research

How Quickly Do Physicians Adopt New Drugs? The Case of Second-Generation Antipsychotics

Objective The authors examined physician adoption of second-generation antipsychotic medications and identified physician-level factors associated with early adoption. Methods The authors estimated Cox proportional-hazards models of time to adoption of nine second-generation antipsychotics by 30,369 physicians who prescribed antipsychotics between 1996 and 2008, when the drugs were first introduced, and analyzed the total number of agents prescribed during that time. The models were adjusted for physicians’ specialty, demographic characteristics, education and training, practice setting, and prescribing volume. Data were from IMS Xponent, which captures over 70% of all prescriptions filled in the United States, and the American Medical Association Physician Masterfile. Results On average, physicians waited two or more years before prescribing new second-generation antipsychotics, but there was substantial heterogeneity across products in time to adoption. General practitioners were much slower than psychiatrists to adopt second-generation antipsychotics (hazard ratios (HRs) range .10−.35), and solo practitioners were slower than group practitioners to adopt most products (HR range .77−.89). Physicians with the highest antipsychotic-prescribing volume adopted second-generation antipsychotics much faster than physicians with the lowest volume (HR range .15−.39). Psychiatrists tended to prescribe a broader set of antipsychotics (median=6) than general practitioners and neurologists (median=2) and pediatricians (median=1). Conclusions As policy makers search for ways to control rapid health spending growth, understanding the factors that influence physician adoption of new medications will be crucial in the efforts to maximize the value of care received by individuals with mental disorders as well as to improve medication safety.National Institute of Mental Health (U.S.) (R01 MH093359)Robert Wood Johnson Foundation (Investigator Award in Health Policy Research)Agency for Healthcare Research and Quality (R01HS017695)National Institute of Mental Health (U.S.) ((NIMH) R34 MH082682)National Institute of Mental Health (U.S.) ((NIMH) P30 MH090333)National Institute of Mental Health (U.S.) ((NIMH) R01 MH087488)National Science Foundation (U.S.) (0915674

Regional Variation in Physician Adoption of Antipsychotics: Impact on US Medicare expenditures

Background—Regional variation in US Medicare prescription drug spending is driven by higher prescribing of costly brand-name drugs in some regions. This variation likely arises from differences in the speed of diffusion of newly-approved medications. Second-generation antipsychotics were widely adopted for treatment of severe mental illness and for several off-label uses. Rapid diffusion of new psychiatric drugs likely increases drug spending but its relationship to non-drug spending is unclear. The impact of antipsychotic diffusion on drug and medical spending is of great interest to public payers like Medicare, which finance a majority of mental health spending in the U.S.National Institute of Mental Health (U.S.) (R01 MH093359

Targeted learning in observational studies with multi-valued treatments: An evaluation of antipsychotic drug treatment safety

We investigate estimation of causal effects of multiple competing (multi-valued) treatments in the absence of randomization. Our work is motivated by an intention-to-treat study of the relative cardiometabolic risk of assignment to one of six commonly prescribed antipsychotic drugs in a cohort of nearly 39,000 adults with serious mental illnesses. Doubly-robust estimators, such as targeted minimum loss-based estimation (TMLE), require correct specification of either the treatment model or outcome model to ensure consistent estimation; however, common TMLE implementations estimate treatment probabilities using multiple binomial regressions rather than multinomial regression. We implement a TMLE estimator that uses multinomial treatment assignment and ensemble machine learning to estimate average treatment effects. Our multinomial implementation improves coverage, but does not necessarily reduce bias, relative to the binomial implementation in simulation experiments with varying treatment propensity overlap and event rates. Evaluating the causal effects of the antipsychotics on three-year diabetes risk or death, we find a safety benefit of moving from a second-generation drug considered among the safest of the second-generation drugs to an infrequently prescribed first-generation drug known for having low cardiometabolic risk

Twenty years of mental health policies in Chile: lessons and challenges

Over the last 20 years, Chile has increased the mental health share of its public health budget and implemented policies that radically transformed psychiatric services in the country. Both national and international factors have contributed to this process. The implementation of two national mental health plans has led to downsizing mental hospitals and developing community alternatives, such as primary health care, community mental health teams, day hospitals, acute psychiatric beds in general hospitals, and group homes. The annual number of new persons starting treatment for mental disorders in the public sector has increased by 343 percent between 2004 and 2007, with depression being the condition that motivates the highest frequency of visits. The Chilean experience has been successful in terms of increasing availability and accessibility of services and demonstrating that with a modicum of political support, it is possible to implement an effective and efficient community-based network of primary and secondary care facilities. Notwithstanding the progress made in this country, the mental health treatment gap is still significant