Association Between Caregiver Burden and Patient Recovery After Left Ventricular Assist Device Implantation: Insights From Sustaining Quality of Life of the Aged: Heart Transplant or Mechanical Support.

INTRODUCTION: After left ventricular assist device (LVAD) implantation, caregivers may experience increasing burden because of new roles and responsibilities. We examined the association between caregiver burden at baseline and patient recovery after long-term LVAD implantation in patients ineligible for heart transplantation. METHODS: Between October 1, 2015, and December 31, 2018, data from 60 patients with a long-term LVAD (age, 60-80 years) and caregivers through 1 postoperative year were analyzed. Caregiver burden was measured using the Oberst Caregiving Burden Scale, a validated instrument used for measuring caregiver burden. Patient recovery post-LVAD implantation was defined by change in Kansas City Cardiomyopathy Questionnaire-12 (KCCQ-12) overall summary score and rehospitalizations over 1 year. Multivariable regression models (least-squares for change in KCCQ-12 and Fine-Gray cumulative incidence for rehospitalizations) were used to assess for association with caregiver burden. RESULTS: Patients were 69.4 ± 5.5 years old, 85% men, and 90% White. Over the first year post-LVAD implantation, there was a 32% cumulative probability of rehospitalization; 72% (43/60) of patients had an improvement of ≥5 points in KCCQ-12 scores. Caregivers were 61.2 ± 11.5 years old, 93% women, 81% White, and 85% married. Median Oberst Caregiving Burden Scale Difficulty and Time scores at baseline were 1.13 and 2.27, respectively. Higher caregiver burden was not significantly associated with hospitalizations or change in patient health-related quality of life during the first year post-LVAD implantation. CONCLUSIONS: Higher caregiver burden at baseline was not associated with patient recovery in the first year after LVAD implantation. Understanding the associations between caregiver burden and patient outcomes after LVAD implantation is important as excessive caregiver burden is a relative contraindication for LVAD implantation.

From research to nationwide implementation: the impact of AHRQ's HAI prevention program.

INTRODUCTION: The Agency for Healthcare Research and Quality (AHRQ's) Patient Safety Program is responsive to AHRQ's mission of quality improvement in healthcare. As part of this program, AHRQ has invested in projects to prevent healthcare-associated infections (HAIs), and funding has increased significantly over the last decade. AHRQ-funded projects have focused on generating new knowledge and promoting the nationwide implementation of proven HAI prevention measures in diverse healthcare settings. OBJECTIVES: To provide insight to AHRQ's HAI prevention strategies by: first, discussing the context and structure of AHRQ's HAI research portfolio and funding decisions; secondly, describing the process of prevention practice implementation and lessons learned; and third, explaining the outcomes and national impact of the AHRQ program. RESULTS AND CONCLUSIONS: In the early 2000s, AHRQ identified HAIs as an important and preventable public health threat and built their HAI-prevention portfolio based on National Action Plan priorities, available resources, advice from experts, and the state of science. This paper describes major contributions that have emerged from AHRQ-funded HAI projects. The projects examined, many of which focus on implementation of HAI prevention practices, yield useful lessons learned for future implementation and research endeavors and show significant impact of AHRQ's program in reducing HAIs.

Characterizing Opioid Use Disorder Encounters in the Midwest Region, USA.

INTRODUCTION: The opioid epidemic has taken its toll on the Midwest, a census region of the USA. The Midwest includes two census divisions: East North Central and West North Central. This study aimed (1) to characterize patient encounters of opioid use disorder (OUD) in the Midwest using the Health Facts® database; and (2) to compare selected patient and facility characteristics between the two census divisions. METHODS: This study was a sub-analysis of a retrospective analysis of the Health Facts® database. For the first objective, the unit of analysis was a patient encounter. Selected patient characteristics were age, gender, marital status, race, length of stay, and patient type. Selected facility characteristics were census division and urban versus rural areas. Descriptive statistics were conducted, and population-based rates of OUD were calculated for categorical variables. For the second objective, t tests were performed for age and length of stay, and chi-square tests for categorical variables. RESULTS: A total of 13,129 (23.7%) encounters were in East North Central, and 42,271 (76.3%) in West North Central. Patient characteristics that were associated with the highest frequency of encounters were Caucasian, male, single, and other patient types. In addition, rural areas had a higher number of encounters than urban areas. Compared with East North Central, West North Central had a greater average age and a longer average length of stay (p < 0.001). West North Central had a significantly higher proportion of patient encounters associated with patients being male, African American, single, and facilities being in rural areas (p < 0.001). CONCLUSION: Compared to East North Central, patient encounters of OUD were more frequent and the average length of stay was longer than in West North Central. A significantly higher proportion of patient encounters in West North Central were associated with patients being male, African American, and single, and facilities being in rural areas.

The Influence of Health Behavior Theory on Implementation Practice and Science: Brief Review and Commentary.

As research defines new treatments and policies to improve the health of patients, an increasing challenge has been to translate these insights into routine clinical practice to benefit patients and society. An important exploration is how theories of human behavior change fit into the science of implementation and quality improvement. In this paper, we begin with a brief review of the intellectual roots of implementation science and quality improvement, followed by a discussion of how theories and principles of behavior change can inform both goals and challenges in using behavior change theories. The insights offered through health behavior change theory have led to changes in how we plan for implementation and select, develop, design and tailor implementation interventions and strategies. While the degree to which organizational and external contexts influence the behavior of providers in these organizations varies widely, some degree of context external to the individual is important and needs adequate consideration. In short, health behavior change theory is essential but not sufficient to integrate in most implementation efforts, where priority must be given to both individual factors and contexts in which individuals operate.

Examining Geographic Variation of Opioid Use Disorder Encounters in the USA.

OBJECTIVES: The objectives were (1) to characterize patient encounters of opioid use disorder (OUD) using Health Facts® database; and (2) to identify geographic variation, patient characteristics, and facility characteristics impacting patients' reduced OUD encounters over time. METHODS: Patient encounters were included if the patient (1) was 18 years old or greater; (2) had an index encounter; (3) survived at least 30 days after the discharge. The OUD encounter was based on ICD-10 codes. The date at which a patient first had an OUD encounter was the index date. For the first objective, OUD encounters were described according to patient characteristics, facility characteristics, and geographic variation. Patient characteristics were age, gender, marital status, race, health insurance coverage, discharge disposition, and patient type. Facility characteristics were care setting, medical specialty, census region, census division, urban vs. rural, acute vs. non-acute, and teaching hospital status. For the second objective, patients were examined 1 year prior to through 1 year after the index date. A logistic regression was used to determine the likelihood of reduced OUD encounters over time, conditional upon geographic variation, patient characteristics, and facility characteristics. RESULTS: A total of 265,643 OUD encounters were identified. East South Central was associated with the highest population-based rate of OUD among nine census divisions. In the logistic regression (n = 10,762), discharged to home, outpatient, emergency room, psychiatry, East North Central, West North Central, and urban areas were significant positive predictors for reduced OUD encounters over time, whereas age and Mountain were significant negative predictors. CONCLUSIONS: East South Central was associated with the highest population-based rate of OUD. Compared with East South Central, East North Central and West North Central had a significantly positive impact on fewer encounters of OUD over time.

National estimates of out-of-pocket health care expenditure burdens among nonelderly adults with cancer: 2001 to 2008.

PURPOSE: To compare the prevalence of high out-of-pocket burdens among patients with cancer with other chronically ill and well patients, and to examine the sociodemographic characteristics associated with high burdens among patients with cancer. METHODS: The sample included persons 18 to 64 years of age who received treatment for cancer, taken from a nationally representative sample of the US population from the 2001 to 2008 Medical Expenditure Panel Survey. We examined the proportion of persons living in families with high out-of-pocket burdens associated with medical spending, including insurance premiums, relative to income, defining high health care (total) burden as spending more than 20% of income on health care (and premiums). RESULTS: The risk of high burdens is significantly greater for patients with cancer compared with other chronically ill and well patients. We find that 13.4% of patients with cancer had high total burdens, in contrast to 9.7% among those with other chronic conditions and 4.4% among those without chronic conditions. Among nonelderly persons with cancer, the following were associated with higher out-of-pocket burdens: private nongroup insurance, age 55 to 64 years, non-Hispanic black, never married or widowed, one child or no children, unemployed, lower income, lower education level, living in nonmetropolitan statistical areas, and having other chronic conditions. CONCLUSION: High burdens may affect treatment choice and deter patients from getting care. Thus, although a detailed patient-physician discussion of costs of care may not be feasible, we believe that an awareness of out-of-pocket burdens among patients with cancer is useful for clinical oncologists.