Publications

PURPOSE

Although medical students will influence the future U.S. health care system, their opinions on the Patient Protection and Affordable Care Act (ACA) have not been assessed since the 2016 presidential election and elimination of key ACA provisions. Understanding medical students' views on health care policy and professional obligations can provide insight into issues that will be shaped by the next generation of physicians.

METHOD

From October 2017 to November 2017, the authors conducted an electronic survey of medical students from seven U.S. institutions to elicit opinions regarding the ACA and their professional responsibility to address health policy. Participant demographics and responses were tabulated, and multiple logistic regression models were used to assess the associations of demographic characteristics with student opinions.

RESULTS

Completed surveys were returned by 1,660/4,503 (36.9%) eligible medical students. Respondent demographics were similar to national estimates. In total, 89.1% (1,475/1,660) supported the ACA, and 82.0% (1,362/1,660) reported that they understood the health care law. Knowledge of the law's provisions was positively associated with support for the ACA (P < .001). Most students (85.8%; 1,423/1,660) reported addressing health policy to be a professional responsibility. Political affiliation was consistently associated with student opinions.

CONCLUSIONS

Most medical students support the ACA, with greater levels of support among medical students who demonstrated higher levels of objective knowledge about the law. Furthermore, students indicated a professional responsibility to engage in health policy, suggesting that tomorrow's physicians are likely to participate in future health care reform efforts.

The incidence of tuberculosis (TB) in the United States has stabilized, and additional interventions are needed to make progress toward TB elimination. However, the impact of such interventions depends on local demography and the heterogeneity of populations at risk. Using state-level individual-based TB transmission models calibrated to California, Florida, New York, and Texas, we modeled 2 TB interventions: 1) increased targeted testing and treatment (TTT) of high-risk populations, including people who are non-US-born, diabetic, human immunodeficiency virus (HIV)-positive, homeless, or incarcerated; and 2) enhanced contact investigation (ECI) for contacts of TB patients, including higher completion of preventive therapy. For each intervention, we projected reductions in active TB incidence over 10 years (2016-2026) and numbers needed to screen and treat in order to avert 1 case. We estimated that TTT delivered to half of the non-US-born adult population could lower TB incidence by 19.8%-26.7% over a 10-year period. TTT delivered to smaller populations with higher TB risk (e.g., HIV-positive persons, homeless persons) and ECI were generally more efficient but had less overall impact on incidence. TTT targeted to smaller, highest-risk populations and ECI can be highly efficient; however, major reductions in incidence will only be achieved by also targeting larger, moderate-risk populations. Ultimately, to eliminate TB in the United States, a combination of these approaches will be necessary.

OBJECTIVES

Many healthcare systems use prediction models to estimate and manage patient-level probability of hospitalization. Patients identified as high-risk at one point in time may not, however, remain high-risk. We aimed to describe subgroups of patients with distinct longitudinal risk score patterns to inform interventions tailored to patients' needs.

STUDY DESIGN

Retrospective national cohort study.

METHODS

Using a previously validated prediction algorithm, we identified a cohort of 258,759 patients enrolled in the Veterans Health Administration (VHA) who were in the top 5% of risk for hospitalization within 90 days. During each of the following 24 months, patients were placed in 1 of 6 categories: death, hospitalized, no VHA care, persistently high-risk for hospitalization (≥10% probability), initially high-risk then persistently low-risk (<10% probability), and intermittently high-risk. We used multivariable logistic regression to identify characteristics predictive of being persistently high-risk through the last study month.

RESULTS

After 2 years, 17.7% had died, 13.8% had remained persistently high-risk for hospitalization, 41.5% had become persistently low-risk, and 19.9% were intermittently high-risk. Predictors of being persistently high-risk included urban residence, chronic medical comorbidities, auditory and visual impairment, chronic pain, any cancer diagnosis, and social instability.

CONCLUSIONS

Few patients who were high-risk for hospitalization at baseline remained so. Nonrandomized evaluations of interventions that identify patients based on a single high-risk score may spuriously appear to have positive effects. Clinical interventions may need to focus on individuals who are persistently high-risk.