Clinical Handover Communication at Maternity Shift Changes and Women's Safety in Banjul, The Gambia: A Mixed-Methods Study

Background Clinical handover is a vital communication process for patient safety; transferring patient responsibility between healthcare professionals (HCPs). Exploring handover processes in maternity care is fundamental for service quality, addressing continuity of care and maternal mortality.MethodsThis mixed-methods study was conducted in all three maternity hospitals in Banjul, The Gambia. Shift-to-shift maternity handovers were observed and compared against a standard investigating content and environment. Semi-structured interviews and focus group discussions with doctors, midwives and nurses explored handover experience.Results110 nurse/midwife shift-to-shift handovers were observed across all shift times and maternity wards; only 666 of 845 women (79%) were handed over. Doctors had no scheduled handover. Shift-leads alone gave/received handover, delayed [median 35 minutes, IQR 24-45] 82% of the time; 96% of handovers were not confidential and 29% were disrupted. Standardised guidelines and training were lacking.A median 6 of 28 topics [IQR 5-9] were communicated per woman. Information varied significantly by time, high-risk classification and location. For women in labour, 10 [IQR 8-14] items were handed-over, 8 [IQR 5-11] for women classed ‘high-risk’, 5 [IQR 4-7] for ante/postnatal women (p<0.001); >50% had no care management plan communicated.21 interviews and two focus groups were conducted. Facilitators and barriers to effective handover surrounding three health service factors emerged; health systems, organisation culture and individual clinician factors.ConclusionMaternity handover was inconsistent, hindered by contextual barriers including lack of team communication and guidelines, delays, with some women omitted entirely. Findings alongside HCPs views demonstrate feasible opportunities for enhancing handover, thereby improving women's safety.

Risk stratification for advanced colorectal neoplasia based on the findings of the index and first surveillance colonoscopies

Risk stratification by index colonoscopy is well established for first surveillance endoscopy, but whether the previous two colonoscopies affect the subsequent advanced neoplasias has not been established. Therefore, the subsequent risk based on the findings of the index and first surveillance colonoscopies were investigated. This retrospective, cohort study was conducted in two clinics and included participants who had undergone two or more colonoscopies after index colonoscopy. High-risk was defined as advanced adenoma (≥ 1 cm, or tubulovillous or villous histology, or high-grade dysplasia). Based on the findings of the index and first surveillance colonoscopies, patients were classified into four categories: category A (both colonoscopy findings were normal), category B (no high-risk findings both times), category C (one time high-risk finding), and category D (high-risk findings both times). The incidence of subsequent advanced neoplasia was examined in each category. A total of 13,426 subjects were included and surveyed during the study periods. The subjects in category D had the highest risk of advanced neoplasia (27.4%, n = 32/117). The subjects in category A had the lowest risk (4.0%, n = 225/5,583). The hazard ratio for advanced neoplasia of category D compared to category A was 9.90 (95% Confidence interval 6.82–14.35, P<0.001). Classification based on the findings of index and first surveillance colonoscopies more effectively stratifies the risk of subsequent advanced neoplasia, resulting in more proper allocation of colonoscopy resources after two consecutive colonoscopies.

Effects of Prior Season Vaccination on Current Season Vaccine Effectiveness in the United States Flu Vaccine Effectiveness Network, 2012–2013 Through 2017–2018

Background We compared effects of prior vaccination and added or lost protection from current season vaccination among those previously vaccinated. Methods Our analysis included data from the US Flu Vaccine Effectiveness Network among participants ≥9 years old with acute respiratory illness from 2012–2013 through 2017–2018. Vaccine protection was estimated using multivariate logistic regression with an interaction term for effect of prior season vaccination on current season vaccine effectiveness. Models were adjusted for age, calendar time, high-risk status, site, and season for combined estimates. We estimated protection by combinations of current and prior vaccination compared to unvaccinated in both seasons or current vaccination among prior vaccinated. Results A total of 31 819 participants were included. Vaccine protection against any influenza averaged 42% (95% confidence interval [CI], 38%–47%) among those vaccinated only the current season, 37% (95% CI, 33–40) among those vaccinated both seasons, and 26% (95% CI, 18%–32%) among those vaccinated only the prior season, compared with participants vaccinated neither season. Current season vaccination reduced the odds of any influenza among patients unvaccinated the prior season by 42% (95% CI, 37%–46%), including 57%, 27%, and 55% against A(H1N1), A(H3N2), and influenza B, respectively. Among participants vaccinated the prior season, current season vaccination further reduced the odds of any influenza by 15% (95% CI, 7%–23%), including 29% against A(H1N1) and 26% against B viruses, but not against A(H3N2). Conclusions Our findings support Advisory Committee on Immunization Practices recommendations for annual influenza vaccination. Benefits of current season vaccination varied among participants with and without prior season vaccination, by virus type/subtype and season.

2760. Accounting for Vaccination History in Estimates of Current Season Vaccine Effectiveness in the US Flu VE Network, 2012–2013 Through 2017–2018

Background Current season vaccine effectiveness (VE) and influenza risk may vary in persons based on vaccination history. United States Influenza Vaccine Effectiveness (US Flu VE) Network studies have explored prior vaccination effects using a single referent group of patients unvaccinated in both the prior and current seasons. We investigated vaccine benefit among those with and without prior season vaccination. Methods Our analysis included data from the US Flu VE Network among patients aged ≥9 years old with acute respiratory illness during 6 influenza seasons, 2012–2013 through 2017–2018. We determined current and prior season vaccination status from documented immunizations. Current season VE against laboratory confirmed influenza was estimated using multivariate logistic regression with an interaction term for prior and current season vaccination. Models were adjusted for age, calendar time, high-risk status, and site. Results Of 31,819 patients included in the analysis over 6 seasons, 9188 were influenza positive by RT–PCR. Percent flu positivity was greatest among those unvaccinated (34%), followed by those vaccinated in the prior season only (29%), those vaccinated in both seasons (25%), and those vaccinated in the current season only (23%). Among patients with prior season vaccination, current season VE against any influenza was 14% (95% CL: 5, 22) and against A(H3N2), A(H1N1)pdm09, and B was 10% (95% CL: 3, 17), 36% (95%CL: 25, 46), and 40% (95% CL: 33, 46), respectively. Among patients unvaccinated in the prior season, VE was 42% (95% CL: 37, 46) against any influenza in the current season and was 31% (95%CL: 22, 39), 57% (95% CL: 47, 65), and 55% (95% CL: 48, 61) against A(H3N2), A(H1N1)pdm09, and B, respectively. We observed significant interaction of prior season vaccination on current season VE in 4 of 6 seasons (P < 0.20). Conclusion Current season vaccination was overall protective regardless of vaccination history. Among those vaccinated in the prior season, current season vaccination may provide some benefit in addition to residual protection from previous vaccination. Disclosures All authors: No reported disclosures.

Abstract 89: Validating The Identification Of Patients At High Risk For Readmission By Examining Hospitalization History

OBJECTIVES: To identify patients at high risk of readmission by validating a simple predictive tool based solely on hospitalization history. BACKGROUND: There is a federal mandate to reduce preventable readmissions. Predicting hospital readmission risk is of great interest to identify which patients would benefit most from transition interventions. Current models perform poorly. Mount Sinai Hospital (MSH) has implemented the Preventable Admissions Care Team (PACT), which has achieved significant results for patients not targeted by other transitional programs. PACT, a social worker-led transitional program, decreased 30-day readmission rate from 30% to 12%, ED visits by 63%, and achieved a 90% primary care show rate at 7-10-days post-discharge. Patients are identified for PACT solely by readmission history: one readmission in 30 days or 2 in 6 months, prior to the index hospitalization. Thus, our objective here was to determine the concordance of predictions based on hospitalization history with a more formal risk model based on factors that characterize patients through demographics and comorbidities. METHODS: Using logistic regression, we developed a risk prediction model for readmission within 30-days. The model, which used patient demographics and co-morbidities (alcohol abuse, AMI, afib, breast cancer, CKD, COPD, CVA, depression, hip fracture, or osteoporosis), was developed in a cohort of Medicare FFS beneficiaries with a high proportion of cardiovascular disease, hospitalized at MSH. The higher the risk score, the higher risk of readmission. Scores of 0-2 had a 7% risk of readmission; scores of 3 or 4 and above 5 had 30-day readmission rates of 19%, and 29%, respectively. We then applied this risk scoring model to patients enrolled in PACT to determine how many of them would have been identified as high risk for readmission based on the regression model. RESULTS: A total of 393 patients were enrolled in PACT in a year and completed a 5 week intervention. Eighty seven percent had 1 cardiac comorbid illness (76% CAD, 66% CHF, and 17% Afib). Readmission data was available through 2010 thus, the analysis was completed for 111 patients. Ninety-five percent of PACT enrollees had a risk score greater than 3: 19 patients (17.1%) had a risk score of 3-4, and 87 patients (78.4%) had a risk score of 5 or greater. CONCLUSIONS: Hospitalization history alone is a reasonable proxy to more formal multivariable regression models in predicting 30-day readmission risk. If substantiated through further study, this could have national implications for real time high risk patient identification for transitional services.