Propensity score matching for multilevel spatial data: accounting for geographic confounding in health disparity studies.

BACKGROUND: Diabetes is a public health burden that disproportionately affects military veterans and racial minorities. Studies of racial disparities are inherently observational, and thus may require the use of methods such as Propensity Score Analysis (PSA). While traditional PSA accounts for patient-level factors, this may not be sufficient when patients are clustered at the geographic level and thus important confounders, whether observed or unobserved, vary by geographic location. METHODS: We employ a spatial propensity score matching method to account for "geographic confounding", which occurs when the confounding factors, whether observed or unobserved, vary by geographic region. We augment the propensity score and outcome models with spatial random effects, which are assigned scaled Besag-York-Mollié priors to address spatial clustering and improve inferences by borrowing information across neighboring geographic regions. We apply this approach to a study exploring racial disparities in diabetes specialty care between non-Hispanic black and non-Hispanic white veterans. We construct multiple global estimates of the risk difference in diabetes care: a crude unadjusted estimate, an estimate based solely on patient-level matching, and an estimate that incorporates both patient and spatial information. RESULTS: In simulation we show that in the presence of an unmeasured geographic confounder, ignoring spatial heterogeneity results in increased relative bias and mean squared error, whereas incorporating spatial random effects improves inferences. In our study of racial disparities in diabetes specialty care, the crude unadjusted estimate suggests that specialty care is more prevalent among non-Hispanic blacks, while patient-level matching indicates that it is less prevalent. Hierarchical spatial matching supports the latter conclusion, with a further increase in the magnitude of the disparity. CONCLUSIONS: These results highlight the importance of accounting for spatial heterogeneity in propensity score analysis, and suggest the need for clinical care and management strategies that are culturally sensitive and racially inclusive.

A measurable impact of a self-practice/self-reflection programme on the therapeutic skills of experienced cognitive-behavioural therapists.

The need for effective training methods for enhancing cognitive-behavioural therapist competency is not only relevant to new therapists but also to experienced therapists looking to retain and further enhance their skills. Self-practice/self-reflection (SP/SR) is a self-experiential cognitive-behavioural therapy (CBT) training programme, which combines the experience of practicing CBT methods on oneself with structured reflection on the implications of the experience for clinical practice. In order to build on previous qualitative studies of SP/SR, which have mainly focused on trainee CBT therapists, the aim of the current study was to quantify the impact of SP/SR on the therapeutic skills of an experienced cohort of CBT therapists. Fourteen CBT therapists were recruited to participate in an SP/SR programme specifically adapted for experienced therapists. In the context of a quasi-experimental design including multiple baselines within a single-case methodology, therapists provided self-ratings of technical cognitive therapy skill and interpersonal empathic skill at four critical time points: baseline, pre-SP/SR and post-SP/SR and follow-up. Analysis of programme completers (n = 7) indicated that SP/SR enhances both technical skill and interpersonal therapeutic skill. Further intention-to-treat group (n = 14) analyses including both those who left the programme early (n = 3) and those who partially completed the programme (n = 4) added to the robustness of findings with respect to technical cognitive therapy skills but not interpersonal empathic skills. It was concluded that SP/SR, as a training and development programme, could offer an avenue to further therapeutic skill enhancement in already experienced CBT therapists.

Analysis of racial differences in hospital stays in the presence of geographic confounding.

Using recent methods for spatial propensity score modeling, we examine differences in hospital stays between non-Hispanic black and non-Hispanic white veterans with type 2 diabetes. We augment a traditional patient-level propensity score model with a spatial random effect to create a matched sample based on the estimated propensity score. We then use a spatial negative binomial hurdle model to estimate differences in both hospital admissions and inpatient days. We demonstrate that in the presence of unmeasured geographic confounding, spatial propensity score matching in addition to the spatial negative binomial hurdle outcome model yields improved performance compared to the outcome model alone. In the motivating application, we construct three estimates of racial differences in hospitalizations: the risk difference in admission, the mean difference in number of inpatient days among those hospitalized, and the mean difference in number of inpatient days across all patients (hospitalized and non-hospitalized). Results indicate that non-Hispanic black veterans with type 2 diabetes have a lower risk of hospital admission and a greater number of inpatient days on average. The latter result is especially important considering that we observed much smaller effect sizes in analyses that did not incorporate spatial matching. These results emphasize the need to address geographic confounding in health disparity studies.

Addressing geographic confounding through spatial propensity scores: a study of racial disparities in diabetes.

Motivated by a study exploring differences in glycemic control between non-Hispanic black and non-Hispanic white veterans with type 2 diabetes, we aim to address a type of confounding that arises in spatially referenced observational studies. Specifically, we develop a spatial doubly robust propensity score estimator to reduce bias associated with geographic confounding, which occurs when measured or unmeasured confounding factors vary by geographic location, leading to imbalanced group comparisons. We augment the doubly robust estimator with spatial random effects, which are assigned conditionally autoregressive priors to improve inferences by borrowing information across neighboring geographic regions. Through a series of simulations, we show that ignoring spatial variation results in increased absolute bias and mean squared error, while the spatial doubly robust estimator performs well under various levels of spatial heterogeneity and moderate sample sizes. In the motivating application, we construct three global estimates of the risk difference between race groups: an unadjusted estimate, a doubly robust estimate that adjusts only for patient-level information, and a hierarchical spatial doubly robust estimate. Results indicate a gradual reduction in the risk difference at each stage, with the inclusion of spatial random effects providing a 20% reduction compared to an estimate that ignores spatial heterogeneity. Smoothed maps indicate poor glycemic control across Alabama and southern Georgia, areas comprising the so-called "stroke belt." These results suggest the need for community-specific interventions to target diabetes in geographic areas of greatest need.

State Variations in Infant Feeding Regulations for Child Care.

OBJECTIVES: Early care and education (ECE) settings have become primary targets for policy change in recent years. In our 2008 study, we assessed state and regional variation in infant feeding regulations for ECE and compared them to national standards. We conducted the same regulatory review to assess change over time. Because all but 2 states have updated their regulations, we hypothesized that states would have made substantial improvements in the number of regulations supporting infant feeding in ECE. METHODS: For this cross-sectional study, we reviewed infant feeding regulations for all US states for child care centers (centers) and family child care homes (homes). We compared regulations with 10 national standards and assessed the number of new regulations consistent with these standards since our previous review. RESULTS: Comparing results from 2008 and 2016, we observed significant improvements in 7 of the 10 standards for centers and 4 of the 10 standards for homes. Delaware was the only state with regulations meeting 9 of the 10 standards for centers in 2008. In 2016, Delaware and Michigan had regulations meeting 8 of the 10 standards. Previously, Arkansas, the District of Columbia, Minnesota, Mississippi, Ohio, and South Carolina had regulations consistent with 4 of the 10 standards for homes. In 2016, Delaware, Mississippi, and Vermont had regulations meeting 7 of the 10 standards. CONCLUSIONS: Evidence suggests that enacting new regulations may improve child health outcomes. Given that many states recently enacted regulations governing infant feeding, our findings point to the growing interest in this area.