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Adjusting for geographic variation in observational comparative effectiveness studies: a case study of antipsychotics using state Medicaid data.

ABSTRACT: BACKGROUND: Area-level variation in treatment and outcomes may be a potential source of confounding bias in observational comparative effectiveness studies. This paper demonstrates how to use exploratory spatial data analysis (ESDA) and spatial statistical methods to investigate and control for these potential biases. The case presented compares the effectiveness of two antipsychotic treatment strategies: oral second-generation antipsychotics (SGAs) vs. long-acting paliperiodone palmitate (PP). METHODS: A new-start cohort study was conducted analyzing patient-level administrative claims data (8/1/2008-4/30/2011) from Missouri Medicaid. ESDA techniques were used to examine spatial patterns of antipsychotic prescriptions and outcomes (hospitalization and emergency department (ED) visits). Likelihood of mental health-related outcomes were compared between patients starting PP (N?=?295) and oral SGAs (N?=?8,626) using multilevel logistic regression models adjusting for patient composition (demographic and clinical factors) and geographic region. RESULTS: ESDA indicated significant spatial variation in antipsychotic prescription patterns and moderate variation in hospitalization and ED visits thereby indicating possible confounding by geography. In the multilevel models for this antipsychotic case example, patient composition represented a stronger source of confounding than geographic context. CONCLUSION: Because geographic variation in health care delivery is ubiquitous, it could be a comparative effectiveness research (CER) best practice to test for possible geographic confounding in observational data. Though the magnitude of the area-level geography effects were small in this case, they were still statistically significant and should therefore be examined as part of this observational CER study. More research is needed to better estimate the range of confounding due to geography across different types of observational comparative effectiveness studies and healthcare utilization outcomes.

SUBMITTER: Root ED

PROVIDER: S-EPMC4161848 | biostudies-other | 2014

REPOSITORIES: biostudies-other

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Adjusting for geographic variation in observational comparative effectiveness studies: a case study of antipsychotics using state Medicaid data.

Root Elisabeth Dowling ED Thomas Deborah S K DS Campagna Elizabeth J EJ Morrato Elaine H EH

BMC health services research 20140827

<h4>Background</h4>Area-level variation in treatment and outcomes may be a potential source of confounding bias in observational comparative effectiveness studies. This paper demonstrates how to use exploratory spatial data analysis (ESDA) and spatial statistical methods to investigate and control for these potential biases. The case presented compares the effectiveness of two antipsychotic treatment strategies: oral second-generation antipsychotics (SGAs) vs. long-acting paliperiodone palmitate ...[more]

PMID: 25164423

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Project description:BackgroundThere have been ongoing efforts to understand when and how data from observational studies can be applied to clinical and regulatory decision making. The objective of this review was to assess the comparability of relative treatment effects of pharmaceuticals from observational studies and randomized controlled trials (RCTs).MethodsWe searched PubMed and Embase for systematic literature reviews published between January 1, 1990, and January 31, 2020, that reported relative treatment effects of pharmaceuticals from both observational studies and RCTs. We extracted pooled relative effect estimates from observational studies and RCTs for each outcome, intervention-comparator, or indication assessed in the reviews. We calculated the ratio of the relative effect estimate from observational studies over that from RCTs, along with the corresponding 95% confidence interval (CI) for each pair of pooled RCT and observational study estimates, and we evaluated the consistency in relative treatment effects.ResultsThirty systematic reviews across 7 therapeutic areas were identified from the literature. We analyzed 74 pairs of pooled relative effect estimates from RCTs and observational studies from 29 reviews. There was no statistically significant difference (based on the 95% CI) in relative effect estimates between RCTs and observational studies in 79.7% of pairs. There was an extreme difference (ratio < 0.7 or > 1.43) in 43.2% of pairs, and, in 17.6% of pairs, there was a significant difference and the estimates pointed in opposite directions.ConclusionsOverall, our review shows that while there is no significant difference in the relative risk ratios between the majority of RCTs and observational studies compared, there is significant variation in about 20% of comparisons. The source of this variation should be the subject of further inquiry to elucidate how much of the variation is due to differences in patient populations versus biased estimates arising from issues with study design or analytical/statistical methods.

Project description:OBJECTIVES:In observational studies, epidemiologists often attempt to estimate the total effect of an exposure on an outcome of interest. However, when the underlying diagram is unknown and limited knowledge is available, dissecting bias performances is essential to estimating the total effect of an exposure on an outcome when mistakenly adjusting for mediators under logistic regression. Through simulation, we focused on six causal diagrams concerning different roles of mediators. Sensitivity analysis was conducted to assess the bias performances of varying across exposure-mediator effects and mediator-outcome effects when adjusting for the mediator. SETTING:Based on the causal relationships in the real world, we compared the biases of varying across the effects of exposure-mediator with those of varying across the effects of mediator-outcome when adjusting for the mediator. The magnitude of the bias was defined by the difference between the estimated effect (using logistic regression) and the total effect of the exposure on the outcome. RESULTS:In four scenarios (a single mediator, two series mediators, two independent parallel mediators or two correlated parallel mediators), the biases of varying across the effects of exposure-mediator were greater than those of varying across the effects of mediator-outcome when adjusting for the mediator. In contrast, in two other scenarios (a single mediator or two independent parallel mediators in the presence of unobserved confounders), the biases of varying across the effects of exposure-mediator were less than those of varying across the effects of mediator-outcome when adjusting for the mediator. CONCLUSIONS:The biases were more sensitive to the variation of effects of exposure-mediator than the effects of mediator-outcome when adjusting for the mediator in the absence of unobserved confounders, while the biases were more sensitive to the variation of effects of mediator-outcome than those of exposure-mediator in the presence of an unobserved confounder.

Project description:Background and objectivesGeographic variation in stroke rates is well established in the general population, with higher rates in the South than in other areas of the United States. A similar pattern of geographic variation in ischemic strokes has also recently been reported in patients undergoing long-term dialysis, but whether this is also the case for hemorrhagic stroke is unknown.Design, setting, participants, & measurementsMedicare claims from 2000 to 2005 were used to ascertain hemorrhagic stroke events in a large cohort of incident dialysis patients. A Poisson generalized linear mixed model was generated to determine factors associated with stroke and to ascertain state-by-state geographic variability in stroke rates by generating observed-to-expected (O/E) adjusted rate ratios (ARRs) for stroke.ResultsA total of 265,685 Medicare-eligible incident dialysis patients were studied. During a median follow-up of 15.5 months, 2397 (0.9%) patients sustained a hemorrhagic stroke. African Americans (ARR, 1.43; 95% confidence interval [CI], 1.30 to 1.57), Hispanics (ARR, 1.78; 95% CI, 1.57 to 2.03), and individuals of other races (ARR, 1.51; 95% CI, 1.26 to 1.80) had a significantly higher risk for hemorrhagic stroke compared with whites. In models adjusted for age and sex, four states had O/E ARRs for hemorrhagic stroke that were significantly greater than 1.0 (California, 1.15; Maryland, 1.25; North Carolina, 1.25; Texas, 1.19), while only 1 had an ARR less than 1.0 (Wisconsin, 0.79). However, after adjustment for race and ethnicity, no states had ARRs that varied significantly from 1.0.ConclusionRace and ethnicity, or other factors that covary with these, appear to explain a substantial portion of state-by-state geographic variation in hemorrhagic stroke. This finding suggests that the factors underlying the high rate of hemorrhagic strokes in dialysis patients are likely to be system-wide and that further investigations into regional variations in clinical practices are unlikely to identify large opportunities for preventive interventions for this disorder.

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Adjusting for geographic variation in observational comparative effectiveness studies: a case study of antipsychotics using state Medicaid data.

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Adjusting for geographic variation in observational comparative effectiveness studies: a case study of antipsychotics using state Medicaid data.

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