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Studies finding weak or nonexistent relationships between hospital performance on providing recommended care and hospital-level clinical outcomes raise questions about the value and validity of process of care performance measures. Such findings may cause clinicians to question the effectiveness of the care process presumably captured by the performance measure. However, one cannot infer from hospital-level results whether patients who received the specified care had comparable, worse or superior outcomes relative to patients not receiving that care. To make such an inference has been labeled the “ecological fallacy,” an error that is well known among epidemiologists and sociologists, but less so among health care researchers and policy makers. We discuss such inappropriate inferences in the health care performance measurement field and illustrate how and why process measure-outcome relationships can differ at the patient and hospital levels. We also offer recommendations for appropriate multilevel analyses to evaluate process measure-outcome relationships at the patient and hospital levels and for a more effective role for performance measure bodies and research funding organizations in encouraging such multilevel analyses.

Anderson et al.'s finding that states with legalized medical marijuana had lower suicide rates among young men calls to mind the work of the famous French sociologist, Emile Durkheim. In Le Suicide, published in 1897, Durkheim observed that suicide rates were lower in regions with a higher proportion of Catholics and concluded that social controls within the Catholic religion reduced the likelihood of Catholics taking their own lives. Durkheim's individual-level interpretation of his region-level data subsequently became a textbook example of the "ecological fallacy": the assumption that associations observed at a higher level of aggregation (e.g., state level) are mirrored at the individual level. (Am J Public Health. Published online ahead of print February 25, 2015: e1. doi:10.2105/AJPH.2015.302604). Language: en

Influenced by several trials and reviews highlighting positive outcomes, topiramate is increasingly prescribed as a treatment for alcohol use disorders (AUDs). The only previously published meta-analysis of topiramate for AUDs was limited by a sample of only 3 randomized, placebo-controlled trials (RCTs).A systematic search identified 7 RCTs (including a total of 1,125 participants) that compared topiramate to placebo for the treatment for AUDs. This meta-analysis estimated the overall effects of topiramate on abstinence, heavy drinking, craving, and γ-glutamyltranspeptidase (GGT) outcomes and included several sensitivity analyses to account for the small sample of studies.Overall, the small to moderate effects favored topiramate, although the effect on craving was not quite significantly different from 0. The largest effect was found on abstinence (g = 0.468, p0.01), followed by heavy drinking (g = 0.406, p0.01), GGT (g = 0.324, p = 0.02), and craving (g = 0.312, p = 0.07) outcomes. Sensitivity analyses did not change the magnitude or direction of the results, and tests did not indicate significant publication bias. The small sample size did not allow for examination of specific moderators of the effects of topiramate.Topiramate can be a useful tool in the treatment of AUDs. Its efficacy, based on the current sample of studies, seems to be of somewhat greater magnitude than that of the most commonly prescribed medications for AUDs (naltrexone and acamprosate). Further research will help to identify the contexts in which topiramate is most beneficial (e.g., dose, concurrent psychotherapy, patient characteristics).

The purpose of this study was to determine whether randomized trials of treatments for substance dependence differ from those for other medical disorders on quality of enrollment information reporting and sample representativeness.Twenty highly cited clinical trials (publication date 2002-2010) of treatments for each of 14 prevalent disorders were identified by structured literature search. The disorders were alcohol dependence, drug dependence, nicotine dependence, Alzheimer's disease, breast cancer, colorectal cancer, chronic obstructive pulmonary disease, depression, diabetes, HIV/AIDS, hypertension, ischemic heart disease, lung cancer, and schizophrenia. The 280 clinical trials were coded for number of individuals screened for eligibility, number of screened individuals meeting eligibility criteria, and number of eligible individuals refusing to participate.Substance-dependence treatment trials were significantly more likely to track and report enrollment information (75% vs. 45% of clinical trials for other disorders, p.001). Substance-dependence trials did not differ from trials focused on other disorders on mean rate of non-enrollment. Across disorders, the primary driver of non-enrollment appeared to be clinical trial exclusion criteria rather than eligible patients refusing to enroll.Relative to other disorders, trials in the substance-dependence field do a better (although imperfect) job of tracking and reporting enrollment information. Low enrollment rates and unrepresentative samples are not challenges unique to treatment outcome studies in the substance-dependence field. Across a range of disorders, clinical trials that use eligibility criteria judiciously are more likely to produce findings that generalize to front-line clinical practice than are trials that restrict enrollment to a small and unrepresentative subset of patients.

PurposeWe evaluated receipt of cervical cancer screening in a national sample of 34,213 women veterans using Veteran Health Administration facilities between 2003 and 2007 and diagnosed with 1) posttraumatic stress disorder (PTSD), or 2) depression, or 3) no psychiatric illness.MethodsOur study featured a cross-sectional design in which logistic regression analyses compared receipt of recommended cervical cancer screening for all three diagnostic groups.ResultsCervical cancer screening rates varied minimally by diagnostic group: 77% of women with PTSD versus 75% with depression versus 75% without psychiatric illness were screened during the study observation period (p < .001). However, primary care use was associated with differential odds of screening in women with versus without psychiatric illness (PTSD or depression), even after adjustment for age, income and physical comorbidities (Wald Chi-square (2): 126.59; p < .0001). Specifically, among low users of primary care services, women with PTSD or depression were more likely than those with no psychiatric diagnoses to receive screening. Among high users of primary care services, they were less likely to receive screening.ConclusionPsychiatric illness (PTSD or depression) had little to no effect on receipt of cervical cancer screening. Our finding that high use of primary care services was not associated with comparable odds of screening in women with versus without psychiatric illness suggests that providers caring for women with PTSD or depression and high use of primary care services should be especially attentive to their preventive healthcare needs.

Relationships between health care process performance measures (PPMs) and outcomes can differ in magnitude and even direction for patients versus higher level units (e.g., health care facilities). Such discrepancies can arise because facility-level relationships ignore PPM–outcome relationships for patients within facilities, may have different confounders than patient-level PPM–outcome relationships, and may reflect facility effect modification of patient PPM–outcome relationships. If a patient-level PPM is related to better patient outcomes, that care process should be encouraged. However, the finding in a multilevel analysis that the proportion of patients receiving PPM care across facilities nevertheless is linked to poor hospital outcomes would suggest that interventions targeting the health care facility also are needed.

Implementing evidence-based psychosocial or behavioral treatments for clients with substance use disorders (SUDs) presents significant challenges. In this article, we first identify the treatments for which there is some consensus that sufficient empirical support exists to designate them as "evidence-based," and then briefly consider the nature of that evidence. Following that, we review data from a Substance Abuse and Mental Health Services Administration survey on the extent to which these evidence-based treatments (EBTs) are used in SUD treatment in the United States. The main focus of the article is a review of 21 studies attempting to implement EBTs from which we glean information on factors associated with more and less successful implementation. We conclude that more conceptually driven, organizationally focused (not just individual-provider-focused) approaches to implementation are needed and that, at least with some providers in some organizational contexts, it may be more effective to implement evidence-based practices or processes (EBPs) rather than EBTs.

Background In fiscal year (FY) 2008, 133,658 patients were provided services within substance use disorders treatment programs (SUDTPs) in the U.S. Department of Veterans Affairs (VA) health care system. To improve the effectiveness and cost-effectiveness of SUDTPs, we analyze the impacts of staffing mix on the benefits and costs of specialty SUD services. This study demonstrates how cost-effective staffing mixes for each type of VA SUDTPs can be defined empirically. Methods We used a stepwise method to derive prediction functions for benefits and costs based on patients’ treatment outcomes at VA SUDTPs nationally from 2001 to 2003, and used them to formulate optimization problems to determine recommended staffing mixes that maximize net benefits per patient for four types of SUDTPs by using the solver function with the Generalized Reduced Gradient algorithm in Microsoft Excel 2010 while conforming to limits of current practice. We conducted sensitivity analyses by varying the baseline severity of addiction problems between lower (2.5 %) and higher (97.5 %) values derived from bootstrapping. Results and conclusions Compared to the actual staffing mixes in FY01-FY03, the recommended staffing mixes would lower treatment costs while improving patients’ outcomes, and improved net benefits are estimated from $1472 to $17,743 per patient.