Difference-in-difference (DD) methods are a common strategy for evaluating the effects of policies or programs that are instituted at a particular point in time, such as the implementation of a new law. The DD method compares changes over time in a group unaffected by the policy intervention to the changes over time in a group affected by the policy intervention, and attributes the “difference-in-differences” to the effect of the policy. DD methods provide unbiased effect estimates if the trend over time would have been the same between the intervention and comparison groups in the absence of the intervention. However, a concern with DD models is that the program and intervention groups may differ in ways that would affect their trends over time, or their compositions may change over time. Propensity score methods are commonly used to handle this type of confounding in other non-experimental studies, but the particular considerations when using them in the context of a DD model have not been well investigated. In this paper, we describe the use of propensity scores in conjunction with DD models, in particular investigating a propensity score weighting strategy that weights the four groups (defined by time and intervention status) to be balanced on a set of characteristics. We discuss the conceptual issues associated with this approach, including the need for caution when selecting variables to include in the propensity score model, particularly given the multiple time point nature of the analysis. We illustrate the ideas and method with an application estimating the effects of a new payment and delivery system innovation (an accountable care organization model called the “Alternative Quality Contract” (AQC) implemented by Blue Cross Blue Shield of Massachusetts) on health plan enrollee out-of-pocket mental health service expenditures. We find no evidence that the AQC affected out-of-pocket mental health service expenditures of enrollees.
Research has identified misleading and stigmatizing popular beliefs about schizophrenia, but little is known about media images corresponding to these beliefs. Building on Susan Sontag's exploration of cancer in the 1978 book Illness as Metaphor, the authors hypothesize that "schizophrenia" is now more commonly misused. A total of 1740 newspaper articles from 1996 or 1997 that mentioned schizophrenia or cancer were randomly selected and then coded for contextual and metaphorical use. Only 1 percent of articles that mentioned cancer used that illness in a metaphorical way, compared with 28 percent of the articles that mentioned schizophrenia. Results differed by newspaper but not by region. The authors suggest that these inaccurate metaphors in the media contribute to the ongoing stigma and misunderstandings of psychotic illnesses.
Background and Aims Global payment and accountable care reform efforts in the US may connect more individuals with substance use disorders (SUD) to treatment. We tested whether such changes instituted under an ‘Alternative Quality Contract’ (AQC) model within the Blue Cross Blue Shield of Massachusetts’ (BCBSMA) insurer increased care for individuals with SUD. Design Difference-in-differences design comparing enrollees in AQC organizations with a comparison group of enrollees in organizations not participating in the AQC. Setting Massachusetts, USA. Participants BCBSMA enrollees aged 13–64 from 2006–2011 (three years prior to and after implementation) representing 1,333,534 enrollees and 42,801 SUD service users. Measurements Outcomes were SUD service use and spending and SUD performance metrics. Primary exposures were enrollment in an AQC provider organization and whether the AQC organization did or did not face risk for behavioral health costs. Findings Enrollees in AQC organizations facing behavioral health risk experienced no change in the probability of using SUD services (1.64% vs. 1.66%; p=0.63), SUD spending ($2,807 vs. $2,700; p=0.34) or total spending ($12,631 vs. $12,849; p=0.53), or SUD performance metrics (identification: 1.73% vs. 1.76%, p=0.57; initiation: 27.86% vs. 27.02%, p=0.50; engagement: 11.19% vs. 10.97%, p=0.79). Enrollees in AQC organizations not at risk for behavioral health spending experienced a small increase in the probability of using SUD services (1.83% vs. 1.66%; p=0.003) and the identification performance metric (1.92% vs. 1.76%; p=0.007), and a reduction in SUD medication use (11.84% vs. 14.03%; p=0.03) and the initiation performance metric (23.76% vs. 27.02%; p=0.005). Conclusions A global payment and accountable care model introduced in Massachusetts USA (in which a health insurer provided care providers with fixed prepayments to cover most or all of their patients’ care during a specified time period, incentivizing providers to keep their patients’ healthy and reduce costs) did not lead to sizable changes in substance use disorder service use during the first three years following its implementation.
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