Bayesian variable selection in hierarchical difference-in-differences models

A popular method for estimating a causal treatment effect with observational data is the difference-in-differences model. In this work, we consider an extension of the classical difference-in-differences setting to the hierarchical context in which data cannot be matched at the most granular level. Our motivating example is an application to assess the impact of primary care redesign policy on diabetes outcomes in Minnesota, in which the policy is administered at the clinic level and individual outcomes are not matched from pre- to post-intervention. We propose a Bayesian hierarchical difference-in-differences model, which estimates the policy effect by regressing the treatment on a latent variable representing the mean change in group-level outcome. We present theoretical and empirical results showing a hierarchical difference-in-differences model that fails to adjust for a particular class of confounding variables, biases the policy effect estimate. Using a structured Bayesian spike-and-slab model that leverages the temporal structure of the difference-in-differences context, we propose and implement variable selection approaches that target sets of confounding variables leading to unbiased and efficient estimation of the policy effect. We evaluate the methods’ properties through simulation, and we use them to assess the impact of primary care redesign of clinics in Minnesota on the management of diabetes outcomes from 2008 to 2017.

GP- and practice-related variation in ambulatory sensitive hospitalisations of older primary care patients

Background Reducing ambulatory sensitive hospitalisations (ASHs) is a strategy to control spending on hospital care and to improve quality of primary health care. This research investigated whether ASH rates in older people varied by GP and practice characteristics. Methods We identified ASHs from the national dataset of hospital events for 3755 community-dwelling participants aged 75+ enrolled in a cluster randomised controlled trial involving 60 randomly selected general practices in three regions in New Zealand. Poisson mixed models of 36-month ASH rates were fitted for the entire sample, for complex participants, and non-complex participants. We examined variation in ASH rates according to GP- and practice-level characteristics after adjusting for patient-level predictors of ASH. Results Lower rates of ASHs were observed in female GPs (IRR 0.83, CI 0.71 to 0.98). In non-complex participants, but not complex participants, practices in more deprived areas had lower ASH rates (4% lower per deprivation decile higher, IRR 0.96, CI 0.92 to 1.00), whereas main urban centre practices had higher rates (IRR 1.84, CI 1.15 to 2.96). Variance explained by these significant factors was small (0.4% of total variance for GP sex, 0.2% for deprivation, and 0.5% for area type). None of the modifiable practice-level characteristics such as home visiting and systematically contacting patients were significantly associated with ASH rates. Conclusions Only a few GP and non-modifiable practice characteristics were associated with variation in ASH rates in 60 New Zealand practices interested in a trial about care of older people. Where there were significant associations, the contribution to overall variance was minimal. It also remains unclear whether lower ASH rates in older people represents underservicing or less overuse of hospital services, particularly for the relatively well patient attending practices in less central, more disadvantaged communities. Thus, reducing ASHs through primary care redesign for older people should be approached carefully. Trial registration Australian and New Zealand Clinical Trials Register ACTRN12609000648224.

Primary care redesign for person-centred care: delivering an international generalist revolution

Person-centred primary care is a priority for patients, healthcare practitioners and health policy. Despite this, data suggest person-centred care is still not consistently achieved – and indeed, that in some areas, care may be worsening. Whole-person care is the expertise of the medical generalist – an area of clinical practice that has been neglected by health policy for some time. It is internationally recognised that there is a need to rebalance specialist and generalist primary care. Drawing on 15 years of scholarship within the science of medical generalism (the expertise of whole-person medical care), this discussion paper outlines a three-tiered approach to primary care redesign; describing changes needed at the level of the consultation, practice set up and strategic planning. The changing needs of patients living with complex chronic illness has already started a revolution in our understanding of healthcare systems. This paper outlines work to support that paradigm shift from disease-focused to person-focused primary healthcare.

Utilization Outcomes of a Pilot Primary Care Team Redesign

Objectives: To evaluate the effect of a team-based primary care redesign on primary care, emergency department (ED) and urgent care (UC) utilization, and new patient access to primary care. Study Design: A retrospective pre–post difference-in-differences analysis of utilization outcomes for patients on a redesigned primary care team compared to a standard primary care group. Methods: Within a patient-centered medical home, a pilot team was developed comprising 2 colocated “teamlets” of 1 physician, 1 nurse practitioner (NP), 1 registered nurse (RN), and 2 licensed practical nurses (LPNs). The redesigned team utilized physician–NP comanagement, expanded roles for RNs and LPNs, and dedicated provider time for telephone and e-mail medicine. We compared changes in number of office, ED, and UC visits during the implementation year for patients on the redesigned team compared to patients receiving the standard of care in the same clinic. Proportion of new patient visits was also compared between the pilot and the control groups. Results: There were no differences between the redesign group and control group in per-patient mean change in office visits (Δ = −0.04 visits vs Δ = −0.07; P = .98), ED visits (Δ = 0.00 vs Δ = 0.01; P = .25), or UC visits (Δ = 0.00 vs Δ = 0.05; P = .08). Proportion of new patient visits was higher in the pilot group during the intervention year compared to the control group (6.6% vs 3.9%; P < .0001). Conclusions: The redesign did not significantly impact ED, UC, or primary care utilization within 1 year of follow-up. It did improve access for new patients.