Patient-Physician Sex Discordance and "Before Medically Advised" Discharge from Hospital: A Population-Based Retrospective Cohort Study.

BACKGROUND: Patient-physician sex discordance (when patient sex does not match physician sex) has been associated with reduced clinical rapport and adverse outcomes including post-operative mortality and unplanned hospital readmission. It remains unknown whether patient-physician sex discordance is associated with "before medically advised" hospital discharge (BMA discharge; commonly known as discharge "against medical advice"). OBJECTIVE: To evaluate whether patient-physician sex discordance is associated with BMA discharge. DESIGN: Retrospective cohort study using 15 years (2002-2017) of linked population-based administrative health data for all non-elective, non-obstetrical acute care hospitalizations from British Columbia, Canada. PARTICIPANTS: All individuals with eligible hospitalizations during study interval. MAIN MEASURES: Exposure: patient-physician sex discordance. OUTCOMES: BMA discharge (primary), 30-day hospital readmission or death (secondary). RESULTS: We identified 1,926,118 eligible index hospitalizations, 2.6% of which ended in BMA discharge. Among male patients, sex discordance was associated with BMA discharge (crude rate, 4.0% vs 2.9%; adjusted odds ratio [aOR] 1.08; 95%CI 1.03-1.14; p = 0.003). Among female patients, sex discordance was not associated with BMA discharge (crude rate, 2.0% vs 2.3%; aOR 1.02; 95%CI 0.96-1.08; p = 0.557). Compared to patient-physician sex discordance, younger patient age, prior substance use, and prior BMA discharge all had stronger associations with BMA discharge. CONCLUSIONS: Patient-physician sex discordance was associated with a small increase in BMA discharge among male patients. This finding may reflect communication gaps, differences in the care provided by male and female physicians, discriminatory attitudes among male patients, or residual confounding. Improved communication and better treatment of pain and opioid withdrawal may reduce BMA discharge.

Population-Based Trends in Complexity of Hospital Inpatients.

Importance: Clinical experience suggests that hospital inpatients have become more complex over time, but few studies have evaluated this impression. Objective: To assess whether there has been an increase in measures of hospital inpatient complexity over a 15-year period. Design, Setting and Participants: This cohort study used population-based administrative health data from nonelective hospitalizations from April 1, 2002, to January 31, 2017, to describe trends in the complexity of inpatients in British Columbia, Canada. Hospitalizations were included for individuals 18 years and older and for which the most responsible diagnosis did not correspond to pregnancy, childbirth, the puerperal period, or the perinatal period. Data analysis was performed from July to November 2023. Exposure: The passage of time (15-year study interval). Main Outcomes and Measures: Measures of complexity included patient characteristics at the time of admission (eg, advanced age, multimorbidity, polypharmacy, recent hospitalization), features of the index hospitalization (eg, admission via the emergency department, multiple acute medical problems, use of intensive care, prolonged length of stay, in-hospital adverse events, in-hospital death), and 30-day outcomes after hospital discharge (eg, unplanned readmission, all-cause mortality). Logistic regression was used to estimate the relative change in each measure of complexity over the entire 15-year study interval. Results: The final study cohort included 3 367 463 nonelective acute care hospital admissions occurring among 1 272 444 unique individuals (median [IQR] age, 66 [48-79] years; 49.1% female and 50.8% male individuals). Relative to the beginning of the study interval, inpatients at the end of the study interval were more likely to have been admitted via the emergency department (odds ratio [OR], 2.74; 95% CI, 2.71-2.77), to have multimorbidity (OR, 1.50; 95% CI, 1.47-1.53) and polypharmacy (OR, 1.82; 95% CI, 1.78-1.85) at presentation, to receive treatment for 5 or more acute medical issues (OR, 2.06; 95% CI, 2.02-2.09), and to experience an in-hospital adverse event (OR, 1.20; 95% CI, 1.19-1.22). The likelihood of an intensive care unit stay and of in-hospital death declined over the study interval (OR, 0.96; 95% CI, 0.95-0.97, and OR, 0.81; 95% CI, 0.80-0.83, respectively), but the risks of unplanned readmission and death in the 30 days after discharge increased (OR, 1.14; 95% CI, 1.12-1.16, and OR, 1.28; 95% CI, 1.25-1.31, respectively). Conclusions and Relevance: By most measures, hospital inpatients have become more complex over time. Health system planning should account for these trends.

Unconditional cash transfers reduce homelessness.

Homelessness is an economic and social crisis. In a cluster-randomized controlled trial, we address a core cause of homelessness-lack of money-by providing a one-time unconditional cash transfer of CAD$7,500 to each of 50 individuals experiencing homelessness, with another 65 as controls in Vancouver, BC. Exploratory analyses showed that over 1 y, cash recipients spent fewer days homeless, increased savings and spending with no increase in temptation goods spending, and generated societal net savings of $777 per recipient via reduced time in shelters. Additional experiments revealed public mistrust toward the ability of homeless individuals to manage money and demonstrated interventions to increase public support for a cash transfer policy using counter-stereotypical or utilitarian messaging. Together, this research offers a new approach to address homelessness and provides insights into homelessness reduction policies.

Physician Financial Incentives for Use of Outpatient Intravenous Antimicrobial Therapy: An Interrupted Time Series Analysis.

BACKGROUND: In 2011, policymakers in British Columbia introduced a fee-for-service payment to incentivize infectious diseases physicians to supervise outpatient parenteral antimicrobial therapy (OPAT). Whether this policy increased use of OPAT remains uncertain. METHODS: We conducted a retrospective cohort study using population-based administrative data over a 14-year period (2004-2018). We focused on infections that required intravenous antimicrobials for ≥10 days (eg, osteomyelitis, joint infection, endocarditis) and used the monthly proportion of index hospitalizations with a length of stay shorter than the guideline-recommended "usual duration of intravenous antimicrobials" (LOS < UDIVA) as a surrogate for population-level OPAT use. We used interrupted time series analysis to determine whether policy introduction increased the proportion of hospitalizations with LOS < UDIVA. RESULTS: We identified 18 513 eligible hospitalizations. In the pre-policy period, 82.3% of hospitalizations exhibited LOS < UDIVA. Introduction of the incentive was not associated with a change in the proportion of hospitalizations with LOS < UDIVA, suggesting that the policy intervention did not increase OPAT use (step change, -0.06%; 95% confidence interval [CI], -2.69% to 2.58%; P = .97 and slope change, -0.001% per month; 95% CI, -.056% to .055%; P = .98). CONCLUSIONS: The introduction of a financial incentive for physicians did not appear to increase OPAT use. Policymakers should consider modifying the incentive design or addressing organizational barriers to expanded OPAT use.

Combining parametric and nonparametric models to estimate treatment effects in observational studies.

Performing causal inference in observational studies requires we assume confounding variables are correctly adjusted for. In settings with few discrete-valued confounders, standard models can be employed. However, as the number of confounders increases these models become less feasible as there are fewer observations available for each unique combination of confounding variables. In this paper, we propose a new model for estimating treatment effects in observational studies that incorporates both parametric and nonparametric outcome models. By conceptually splitting the data, we can combine these models while maintaining a conjugate framework, allowing us to avoid the use of Markov chain Monte Carlo (MCMC) methods. Approximations using the central limit theorem and random sampling allow our method to be scaled to high-dimensional confounders. Through simulation studies we show our method can be competitive with benchmark models while maintaining efficient computation, and illustrate the method on a large epidemiological health survey.