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Veterans’ Affairs (VA) healthcare providers perceive that Veterans expect and base visit satisfaction on receiving antibiotics for upper respiratory tract infections (URIs). No studies have tested this hypothesis. We sought to determine whether receiving and/or expecting antibiotics were associated with Veteran satisfaction with URI visits.
This cross-sectional study included Veterans evaluated for URI January 2018–December 2019 in an 18-clinic ambulatory VA primary-care system. We evaluated Veteran satisfaction via the Patient Satisfaction Questionnaire Short Form (RAND Corporation), an 18-item 5-point Likert scale survey. Additional items assessed Veteran antibiotic expectations. Antibiotic receipt was determined via medical record review. We used multivariable regression to evaluate whether antibiotic receipt and/or Veteran antibiotic expectations were associated with satisfaction. Subgroup analyses focused on Veterans who accurately remembered antibiotic prescribing during their URI visit.
Of 1,329 eligible Veterans, 432 (33%) participated. Antibiotic receipt was not associated with differences in mean total satisfaction (adjusted score difference, 0.6 points; 95% confidence interval [CI], −2.1 to 3.3). However, mean total satisfaction was lower for Veterans expecting an antibiotic (adjusted score difference −4.4 points; 95% CI −7.2 to −1.6). Among Veterans who accurately remembered the visit and did not receive an antibiotic, those who expected an antibiotic had lower mean satisfaction scores than those who did not (unadjusted score difference, −16.6 points; 95% CI, −24.6 to −8.6).
Veteran expectations for antibiotics, not antibiotic receipt, are associated with changes in satisfaction with outpatient URI visits. Future research should further explore patient expectations and development of patient-centered and provider-focused interventions to change patient antibiotic expectations.
Background: Long-term care facility (LTCF) employees pose potential risk for COVID-19 outbreaks. Association between employee infection prevention (IP) adherence with facility COVID-19 outbreaks remains a knowledge gap. Methods: From April through December 2020, prior to COVID-19 vaccination, we tested asymptomatic Veterans’ Affairs (VA) community living center (CLC) residents twice weekly and employees monthly, which increased to weekly with known exposure, for SARS-CoV-2 via nasopharyngeal PCR. Employees voluntarily completed multiple choice questionnaires assessing self-reported IP adherence at and outside work. Surveys were longitudinally administered in April, June, July, and October 2020. Changes in paired employee responses for each period were analyzed using the McNemar test. We obtained COVID-19 community rates from surrounding Davidson and Rutherford counties from the Tennessee Department of Health public data set. CLC resident COVID-19 cases were obtained from VA IP data. Incidence rate and number of positive tests were calculated. Results: Between April and December 2020, 444 employees completed at least 1 survey; 177 completed surveys in both April and June, 179 completed surveys in both June and July, and 140 completed surveys in both July and October (Fig. 1). Across periods, employee surveys demonstrated an increase in masking at work and outside work between April and June (63% to 95% [P < .01] and 36% to 63% [P < .01], respectively), and June to July (95% to 99% [P < .05] and 71% to 84% [P < .01], respectively) that were both maintained between July and October (Fig. 2). Distancing at work and limiting social contacts outside work significantly decreased from April to June but increased in subsequent periods, although not significantly. COVID-19 community incidence peaked in July and again in December, but CLC resident COVID-19 cases peaked in August, declined, and remained low through December (Fig. 3). Discussion: Wearing a mask at work, which was mandatory, increased, and voluntary employee masking outside work also increased. CLC COVID-19 cases mirrored community increases in July and August; however, community cases increased again later in 2020 while CLC cases remained low. Employees reporting distancing at work and limiting social contacts outside work decreased preceding the initial rise in CLC cases but increased and remained high after July. Conclusions: These data from the pre–COVID-19 vaccination era suggest that widespread, increased support for and emphasis on LTCF IP adherence, especially masking, may have effectively prevented COVID-19 outbreaks in the vulnerable LTCF population.
OBJECTIVES/GOALS: Using the covariate-rich Veteran Health Administration data, estimate the association between Proton Pump Inhibitor (PPI) use and severe COVID-19, rigorously adjusting for confounding using propensity score (PS)-weighting. METHODS/STUDY POPULATION: We assembled a national retrospective cohort of United States veterans who tested positive for SARS-CoV-2, with information on 33 covariates including comorbidity diagnoses, lab values, and medications. Current outpatient PPI use was compared to non-use (two or more fills and pills on hand at admission vs no PPI prescription fill in prior year). The primary composite outcome was mechanical ventilation use or death within 60 days; the secondary composite outcome included ICU admission. PS-weighting mimicked a 1:1 matching cohort, allowing inclusion of all patients while achieving good covariate balance. The weighted cohort was analyzed using logistic regression. RESULTS/ANTICIPATED RESULTS: Our analytic cohort included 97,674 veterans with SARS-CoV-2 testing, of whom 14,958 (15.3%) tested positive (6,262 [41.9%] current PPI-users, 8,696 [58.1%] non-users). After weighting, all covariates were well-balanced with standardized mean differences less than a threshold of 0.1. Prior to PS-weighting (no covariate adjustment), we observed higher odds of the primary (9.3% vs 7.5%; OR 1.27, 95% CI 1.13-1.43) and secondary (25.8% vs 21.4%; OR 1.27, 95% CI 1.18-1.37) outcomes among PPI users vs non-users. After PS-weighting, PPI use vs non-use was not associated with the primary (8.2% vs 8.0%; OR 1.03, 95% CI 0.91-1.16) or secondary (23.4% vs 22.9%;OR 1.03, 95% CI 0.95-1.12) outcomes. DISCUSSION/SIGNIFICANCE: The associations between PPI use and severe COVID-19 outcomes that have been previously reported may be due to limitations in the covariates available for adjustment. With respect to COVID-19, our robust PS-weighted analysis provides patients and providers with further evidence for PPI safety.
Background: Antibiotics are not recommended but are often prescribed for upper respiratory-tract infections (URIs). Prescribers cite patient expectation as a driver of inappropriate antibiotic prescribing; prior literature has demonstrated higher satisfaction scores in patients who receive antibiotics compared to those who do not. We assessed whether veteran satisfaction at URI visits was associated with antibiotic receipt or with reported expectation for antibiotics. Methods: We surveyed veterans with documented URI encounters in the Veterans’ Affairs Tennessee Valley Healthcare System between January 1, 2018, and December 31, 2019. Patients not evaluated in person, with documented dementia, or who died prior to the study start date were excluded. Veterans were asked to recall their URI visit and to complete the Patient Safety Questionnaire (PSQ)-18 (Rand Corporation) and questions assessing antibiotic expectations. The PSQ-18, an 18-item survey that assesses patient satisfaction, uses a 5-point Likert scale (ie, strongly disagree, disagree, uncertain, agree, strongly agree), yielding a composite score of 18–90. Higher scores represent more satisfaction with care. Demographic and visit-specific information were extracted via chart review. We used multivariable linear regression to assess differences in composite PSQ-18 satisfaction scores between those who did and did not receive an antibiotic, adjusted for patient and visit characteristics, and to assess differences in satisfaction scores for those who did and did not report expecting antibiotics, adjusted for antibiotic receipt. Results: We identified 1,435 patients seen for URI at 17 sites. After exclusions, 1,343 veterans were eligible for chart abstraction. After excluding 42 responders who responded after study close or returned blank surveys, the final analytic cohort included 432 (32.2%) of 1,343 responders; 225 (52.1%) received an antibiotic and 207 (47.9%) did not. Mean total satisfaction for veterans who received an antibiotic was 67.8 (SD, ±9.4) compared to 66.7 (SD, ±9.7) for those who did not (Figure 1). Increased total satisfaction was not significantly associated with antibiotic receipt (0.65; 95% CI, −2.0 to 3.3). Most veterans (72.0%) disagreed that visit satisfaction depended on antibiotic receipt. However, only 30.8% reported that they would not expect an antibiotic for URI visits. A significant reduction in total satisfaction (−4.1; 95% CI, −6.3 to −1.9) was associated with expecting compared to not expecting an antibiotic. Conclusions: Our findings suggest that prescribing an antibiotic is not associated with increased veteran satisfaction for URI visits but is associated with expecting an antibiotic. Future work will evaluate methods to change veteran antibiotic expectations.
Many patients with advanced serious illness or at the end of life experience delirium, a potentially reversible form of acute brain dysfunction, which may impair ability to participate in medical decision-making and to engage with their loved ones. Screening for delirium provides an opportunity to address modifiable causes. Unfortunately, delirium remains underrecognized. The main objective of this pilot was to validate the brief Confusion Assessment Method (bCAM), a two-minute delirium-screening tool, in a veteran palliative care sample.
This was a pilot prospective, observational study that included hospitalized patients evaluated by the palliative care service at a single Veterans’ Administration Medical Center. The bCAM was compared against the reference standard, the Diagnostic and Statistical Manual of Mental Disorders, fifth edition. Both assessments were blinded and conducted within 30 minutes of each other.
We enrolled 36 patients who were a median of 67 years (interquartile range 63–73). The primary reasons for admission to the hospital were sepsis or severe infection (33%), severe cardiac disease (including heart failure, cardiogenic shock, and myocardial infarction) (17%), or gastrointestinal/liver disease (17%). The bCAM performed well against the Diagnostic and Statistical Manual of Mental Disorders, fifth edition, for detecting delirium, with a sensitivity (95% confidence interval) of 0.80 (0.4, 0.96) and specificity of 0.87 (0.67, 0.96).
Significance of Results
Delirium was present in 27% of patients enrolled and never recognized by the palliative care service in routine clinical care. The bCAM provided good sensitivity and specificity in a pilot of palliative care patients, providing a method for nonpsychiatrically trained personnel to detect delirium.
OBJECTIVES/SPECIFIC AIMS: Opioid prescribing is common and increasing in certain areas of the country with known risk of misuse and dependence. Our study examined the association of opioid prescription at discharge after hospitalization for acute coronary syndrome (ACS) or acute decompensated heart failure (ADHF) with emergency department (ED) care or all-cause readmission, intended healthcare utilization (follow-up with physician within 30 d of discharge and cardiac rehab participation), and all-cause mortality. METHODS/STUDY POPULATION: The Vanderbilt Inpatient Cohort Study is a prospective cohort of hospitalized patients age >18 enrolled with either ACS or ADHF between 2011 and 2015 (index hospitalization). We then excluded those who died during the index hospitalization, patients with hospitalization <24 hours, patients discharged to hospice care, or those who underwent coronary artery bypass surgery because of the high probability of receiving opioids. In addition, we limited the analyses to patients whom we had complete covariate data. The primary predictor variable was an opioid prescription at the time of hospital discharge. We collected healthcare utilization behavior for 90 days after discharge, and mortality data until March 8, 2017. Time-to-event analysis using Cox proportional hazard models was performed for both unintended healthcare utilization behavior and mortality outcomes. Logistic regression was performed for intended healthcare utilization (adherence to follow-up appointments and cardiac rehabilitation). All models were adjusted for demographic data, opioid use prior to index hospitalization, severity of illness, and healthcare utilization prior to the index hospitalization. RESULTS/ANTICIPATED RESULTS: There were 501 patients discharged with an opioid prescription and 1994 with no opioid prescription at discharge. Among patients with opioids at discharge 235 (47%) experienced unplanned healthcare events (71 ED visits and 164 readmissions) and among nonopioids patients 775 (39%) experienced unplanned healthcare events (254 ED visits and 521 readmissions) (aHR: 1.06, 95% CI: 0.87, 1.28). Patient mortality in the opioid group was 131 Versus 432 in the nonopioid group (aHR: 1.08, 95% CI 0.84, 1.39). Patients in the opioid at discharge group were less likely to attend follow up visits or participate in cardiac rehab (OR: 0.69, 95% CI 0.52, 0.91, p=0.009) compared with those not discharged on opioid medications. Sensitivity analysis of patients who were prescribed prehospital opioids (including prehospital opioids in the exposure group with postdischarge opioids) did not reveal a statistically significant increase in mortality (aHR: 1.09, 95% CI 0.91, 1.31) or unintended healthcare utilization (aHR: 1.12, 95% CI 0.89, 1.41) among opioid users. DISCUSSION/SIGNIFICANCE OF IMPACT: Morbidity and mortality related to opioid use is a public health concern. Our study demonstrates a statistically significant reduction in physician follow-up and participation in cardiac rehab among opioid users, both of which are known to decrease patient mortality. We did not find a statistically significant increase in unplanned healthcare utilization or mortality. Sensitivity analysis combining prehospital and posthospital opioid prescriptions did not reveal a statistically significant association between opioid use, hospital readmissions, or mortality. The hospital provides unique patient interactions where providers can make significant medical changes based on their patient’s clinical status. Continuing to understand the association between opioid use, healthcare utilization, morbidity, and mortality in recently hospitalized cardiac patients will provide data to support reduction in total opioid dose to improve clinical outcomes.
OBJECTIVES/SPECIFIC AIMS: This study is part of a parent grant evaluating antidiabetic medications and risk for heart failure in an observational cohort of Veterans with type 2 diabetes (T2DM). Confounding by indication remains a concern in many observational studies of medications because difficult to measure confounders such as frailty may influence prescribing of different medications based on patient characteristics. Frailty is a multidimensional syndrome of loss of reserves (energy, physical ability, cognition, health) that gives rise to vulnerability to adverse outcomes. The objective of this study is to determine if frailty is a potential confounder in Veterans with T2DM, that is, independently associated with exposure to a specific antidiabetic medications and hospitalization for decompensated heart failure. METHODS/STUDY POPULATION: We conducted a cross-sectional study of patients with diabetes who were hospitalized within the Veterans Health Administration (VHA) Tennessee Valley Healthcare System from 2002 to 2012. Inclusion criteria were: age 18 years or older, receive regular VHA care (prescription fill or visit at least once every 180 d), a diagnosis of T2DM. A probability sample of HF and non-HF hospitalizations was collected. HF hospitalizations were selected on the basis of meeting either a primary diagnosis code (ICD-9) and/or disease related group (DRG) code for HF. For each hospitalization using a standardized chart abstraction tool, data was abstracted on: antidiabetic medication(s), patient frailty status, and reason for hospitalization (HF or non-HF). Antidiabetic medication regimens were categorized as follows: no medication treatment, metformin alone, sulfonylurea alone, insulin alone, insulin and one oral agent, and all other regimens. Patient frailty status was measured using a modified version of the Canadian Health and Aging frailty index (FI), which generates a score (range 0–1) by dividing the number of deficits present by the number of deficits measured. Established categories for FI scores are: non frail ≤0.10, vulnerable 0.10–0.21, frail 0.22–0.45, and very frail >0.45. Patient frailty status at the time of hospitalization was used as a surrogate for patient frailty at the time of prescription of antidiabetic medication; this is a limitation of this approach. Hospitalizations were classified as HF hospitalizations if 2 major or 1 major and 2 minor Framingham criteria were present. FI was compared across antidiabetic medication regimen categories and hospitalization type using analysis of variance (ANOVA) and Student t-test, respectively. RESULTS/ANTICIPATED RESULTS: Of the 500 hospitalizations reviewed, 430 patients had confirmed diabetes diagnosis, adequate data to calculate FI scores, and were included in this analysis. Patients were on average 66.9 (10.9) years old; 99% male and 75% were White. Overall, 268 patients (62.3%) were categorized as frail or very frail. The mean FI score was 0.23 (SD 0.07). FI scores were highest in patients receiving insulin alone (mean 0.26) compared with patients receiving metformin alone (mean 0.22), sulfonylurea alone (mean 0.23), or no medication (mean 0.22). The lowest mean frailty score was seen in patients taking all other drug combinations, 0.19. The differences across these patient groups were statistically significant with p<0.01. Further, 75% of patients on insulin alone were frail or very frail compared with 68% on sulfonylurea alone, 58% on metformin alone, and 58% on no medication. Framingham criteria for acute HF were present for 318 of 430 patients (74.0%). FI scores were higher in patients hospitalized for HF compared with non-HF hospitalizations (mean 0.24 vs. 0.21, p<0.01). A higher proportion of patients hospitalized for HF were classified as frail or very frail compared with those hospitalized for non-HF diagnosis (66.4% vs. 50.9%, p<0.01). DISCUSSION/SIGNIFICANCE OF IMPACT: This study demonstrates that certain antidiabetic medications are associated with patient frailty. In addition, those patients admitted for HF have higher FI scores than those admitted for non-HF diagnoses. Further investigation is planned to assess the degree to which frailty is captured by traditional covariates used in observational studies.
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