Enhanced Primary Care for People With Serious Mental Illness: A Propensity Weighted Cohort Study

Objective: People with serious mental illness (SMI) have high rates of cardiometabolic illness, receive low quality care, and experience poor outcomes. Nevertheless, studies of existing integrated care models have not consistently shown improvements in cardiometabolic health for people with SMI. This study assessed the effect of a novel model of enhanced primary care for people with SMI on cardiometabolic outcomes. Enhanced primary care is a model of integrated care wherein comprehensive primary care delivery is adapted to the needs of people with SMI in coordination with behavioral care.

Methods: We conducted a propensity-weighted cohort study comparing 234 patients with SMI receiving enhanced primary care to 4,934 patients with SMI receiving usual primary care using electronic health data from a large academic medical system covering the years 2014-2018. The propensity-weighted models controlled for baseline differences in outcome measures and patient characteristics between groups.

Results: Compared to usual primary care, enhanced primary care increased hemoglobin A1c (HbA1c) screening by 18 percentage points (95% confidence interval [CI], 10 to 25), low-density lipoprotein (LDL) screening by 16 percentage points (CI, 8.8 to 24), and blood pressure screening by 7.8 percentage points (CI, 5.8 to 9.9). Enhanced primary care reduced HbA1c by 0.27 percentage points (CI, -0.47 to -0.060) and systolic blood pressure by 3.9 mm Hg (CI, -5.2 to -2.5) compared to usual primary care. We did not find evidence that enhanced primary care consistently affected glucose screening, LDL values, or diastolic blood pressure.

Conclusions: Enhanced primary care can achieve clinically meaningful improvements in cardiometabolic health compared to usual primary care.

Predictors of hospital bypass for rural residents seeking common elective surgery

BACKGROUND

Surgical bypass occurs when rural residents receive surgical care at a nonlocal hospital. Given limited knowledge of current bypass rates, we evaluated rates and predictors of bypass for common procedures.

METHODS

We used 2014 to 2016 all-payer claims data from the Healthcare Cost and Utilization Project State Inpatient Databases to study rural patients from 13 states who underwent 1 of 11 common elective surgical procedures. Bypass was measured by whether a patient received elective surgical care at the closest hospital offering the requested procedure or another nonlocal hospital. Bypass probability was then modeled as a function of patient-level and hospital-level characteristics.

RESULTS

Of the 121,297 rural elective surgery visits in our sample, 78,268 (64.5%) bypassed their local hospital. Bypass rate was greatest for coronary artery bypass graft or valve replacement (74.8%) and lowest for laparoscopic cholecystectomy (53.7%). In addition, average bypass rate was greatest for surgeries with the highest risk of intraoperative blood loss and postoperative complications. The probability of bypass significantly (P < .001) increased for patients who were younger, privately insured, and lived farther from the closest hospital. In addition, the probability of bypass significantly (P < .001) increased for patients whose local hospital had fewer full-time equivalents, lower operating margin, and fewer recommendations from previous patients.

CONCLUSION

Among rural patients seeking elective surgery, bypass of the local hospital was common among both low-risk and high-risk procedures. These findings suggest that there is a substantial amount of bypass, which may negatively impact a hospital's financial performance and, hence, wellbeing of the local community.

The economic effects of rural hospital closures

OBJECTIVE

To provide an updated analysis of the economic effects of rural hospital closures.

STUDY SETTING

Our study sample was national in scope and consisted of nonmetro counties from 2001 to 2018.

STUDY DESIGN

We used a difference-in-differences study design to estimate the effect of a hospital closure on county income, population, unemployment, and size of the labor force. Specifically, we compared economic changes over time in nonmetro counties experiencing a hospital closure to changes in a control group of nonmetro counties over the same time period. We also leveraged insight from recent research to control for estimation bias due to heterogeneity in the closure effect over time or across groups defined by when closure was experienced.

DATA EXTRACTION

Data on (adjusted gross) annual income (in real dollars), annual population size, and monthly unemployment rate and labor force size were sourced from the Internal Revenue Service, Census Bureau, and Bureau of Labor Statistics, respectively. We used data from the North Carolina Rural Health Research Program to identify counties that experienced a hospital closure.

PRINCIPAL FINDINGS

Of the 1759 nonmetro counties in our study sample, 109 experienced a hospital closure during the study period. Relative to the nonclosure counterfactual, closures significantly decreased labor force size, on average, by 1.4% (95% CI: [-2.1%, -0.8%]). Results also suggest that Prospective Payment System (PPS) hospital closures significantly decreased population size, on average, by 1.1% (95% CI: [-1.7%, -0.5%]), relative to the nonclosure counterfactual.

CONCLUSIONS

Our analysis suggests that rural hospital closures often have adverse effects on local economic outcomes. Importantly, the negative economic effects of closure appear to be strongest following Prospective Payment System hospital closures and attenuated when the closed hospital is converted to another type of health care facility, allowing for the continued provision of services other than inpatient care.

Prediction of suicidal ideation risk in a prospective cohort study of medical interns

The purpose of this study was to identify individual and residency program factors associated with increased suicide risk, as measured by suicidal ideation. We utilized a prospective, longitudinal cohort study design to assess the prevalence and predictors of suicidal ideation in 6,691 (2012-2014 cohorts, training data set) and 4,904 (2015 cohort, test data set) first-year training physicians (interns) at hospital systems across the United States. We assessed suicidal ideation two months before internship and then quarterly through intern year. The prevalence of reported suicidal ideation in the study population increased from 3.0% at baseline to a mean of 6.9% during internship. 16.4% of interns reported suicidal ideation at least once during their internship. In the training dataset, a series of baseline demographic (male gender) and psychological factors (high neuroticism, depressive symptoms and suicidal ideation) were associated with increased risk of suicidal ideation during internship. Further, prior quarter psychiatric symptoms (depressive symptoms and suicidal ideation) and concurrent work-related factors (increase in self-reported work hours and medical errors) were associated with increased risk of suicidal ideation. A model derived from the training dataset had a predicted area under the Receiver Operating Characteristic curve (AUC) of 0.83 in the test dataset. The suicidal ideation risk predictors analyzed in this study can help programs and interns identify those at risk for suicidal ideation before the onset of training. Further, increases in self-reported work hours and environments associated with increased medical errors are potentially modifiable factors for residency programs to target to reduce suicide risk.

Effect of Enhanced Primary Care for People with Serious Mental Illness on Service Use and Screening

BACKGROUND

Strategies are needed to better address the physical health needs of people with serious mental illness (SMI). Enhanced primary care for people with SMI has the potential to improve care of people with SMI, but evidence is lacking.

OBJECTIVE

To examine the effect of a novel enhanced primary care model for people with SMI on service use and screening.

DESIGN

Using North Carolina Medicaid claims data, we performed a retrospective cohort analysis comparing healthcare use and screening receipt of people with SMI newly receiving enhanced primary care to people with SMI newly receiving usual primary care. We used inverse probability of treatment weighting to estimate average differences in outcomes between the treatment and comparison groups adjusting for observed baseline characteristics.

PARTICIPANTS

People with SMI newly receiving primary care in North Carolina.

INTERVENTIONS

Enhanced primary care that includes features tailored for individuals with SMI.

MAIN MEASURES

Outcome measures included outpatient visits, emergency department (ED) visits, inpatient stays and days, and recommended screenings 18 months after the initial primary care visit.

KEY RESULTS

Compared to usual primary care, enhanced primary care was associated with an increase of 1.2 primary care visits (95% confidence interval [CI]: 0.31 to 2.1) in the 18 months after the initial visit and decreases of 0.33 non-psychiatric inpatient stays (CI: - 0.49 to - 0.16) and 3.0 non-psychiatric inpatient days (CI: - 5.3 to - 0.60). Enhanced primary care had no significant effect on psychiatric service and ED use. Enhanced primary care increased the probability of glucose and HIV screening, decreased the probability of lipid screening, and had no effect on hemoglobin A1c and colorectal cancer screening.

CONCLUSIONS

Enhanced primary care for people with SMI can increase receipt of some preventive screening and decrease use of non-psychiatric inpatient care compared to usual primary care.

Decline in Inpatient Volume at Rural Hospitals

PURPOSE

To investigate (1) all-payer inpatient volume changes at rural hospitals and (2) whether trends in inpatient volume differ by organizational and geographic characteristics of the hospital and characteristics of the patient population.

METHODS

We used a retrospective, longitudinal study design. Our study sample consisted of rural hospitals between 2011 and 2017. Inpatient volume was measured as inpatient average daily census (ADC). Additional measured hospital characteristics included census region, Medicare payment type, ownership type, number of beds, local competition, total margin, and whether the hospital was located in a Medicaid expansion state. Measured characteristics of the local patient population included total population size, percent of population aged 65 years or older, and percent of population in poverty. To identify predictors of inpatient volume trends, we fit a linear multiple regression model using generalized estimating equations.

FINDINGS

Rural hospitals experienced an average change in ADC of -13% between 2011 and 2017. We found that hospital characteristics (eg, census region, Medicare payment type, ownership type, total margin, whether the hospital was located in a Medicaid expansion state) and patient population characteristics (eg, percent of population in poverty) were significant predictors of inpatient volume trends.

CONCLUSIONS

Trends in inpatient volume differ by organizational and geographic characteristics of the hospital and characteristics of the patient population. Researchers and policy makers should continue to explore the causal mechanisms of inpatient volume decline and its role in the financial viability of rural hospitals.