2009 Pandemic Influenza A (H1N1) Virus Outbreak and Response – Rwanda, October, 2009–May, 2010

BACKGROUND: In October 2009, the first case of pandemic influenza A(H1N1)pdm09 (pH1N1) was confirmed in Kigali, Rwanda and countrywide dissemination occurred within several weeks. We describe clinical and epidemiological characteristics of this epidemic. METHODS: From October 2009 through May 2010, we undertook epidemiologic investigations and response to pH1N1. Respiratory specimens were collected from all patients meeting the WHO case definition for pH1N1, which were tested using CDC's real time RT-PCR protocol at the Rwandan National Reference Laboratory (NRL). Following documented viral transmission in the community, testing focused on clinically severe and high-risk group suspect cases. RESULTS: From October 9, 2009 through May 31, 2010, NRL tested 2,045 specimens. In total, 26% (n = 532) of specimens tested influenza positive; of these 96% (n = 510) were influenza A and 4% (n = 22) were influenza B. Of cases testing influenza A positive, 96.8% (n = 494), 3% (n = 15), and 0.2% (n = 1) were A(H1N1)pdm09, Seasonal A(H3) and Seasonal A(non-subtyped), respectively. Among laboratory-confirmed cases, 263 (53.2%) were children <15 years and 275 (52%) were female. In total, 58 (12%) cases were hospitalized with mean duration of hospitalization of 5 days (Range: 2-15 days). All cases recovered and there were no deaths. Overall, 339 (68%) confirmed cases received oseltamivir in any setting. Among all positive cases, 26.9% (143/532) were among groups known to be at high risk of influenza-associated complications, including age <5 years 23% (122/532), asthma 0.8% (4/532), cardiac disease 1.5% (8/532), pregnancy 0.6% (3/532), diabetes mellitus 0.4% (2/532), and chronic malnutrition 0.8% (4/532). CONCLUSIONS: Rwanda experienced a PH1N1 outbreak which was epidemiologically similar to PH1N1 outbreaks in the region. Unlike seasonal influenza, children <15 years were the most affected by pH1N1. Lessons learned from the outbreak response included the need to strengthen integrated disease surveillance, develop laboratory contingency plans, and evaluate the influenza sentinel surveillance system

Improving Post-Hospital Transitions of Care by Facilitating Follow-Up Appointment Creation

Background: Transitions of care are important to patient safety and care. They are a known period of risk and often patients can suffer an adverse event, resulting in hospital readmissions. Within the George Washington University Hospital, data has shown that over 50% of patients admitted to the internal medicine wards that are readmitted within 1 month do not have a follow-up appointment scheduled prior to initial discharge. A study at the University of Colorado Hospital showed that patients lacking timely follow-up after an inpatient stay were ten times more likely to be readmitted. Our project aims to improve the rate of follow-up appointments made and documented by 25%. Methods: Baseline data was collected in December 2015 from four internal medicine wards teams tracking the number of daily patient discharges, follow-up appointments, and time to follow-up appointment from discharge. Our intervention was to provide all internal medicine wards team members with local clinics’ contact information. Data was again collected after the intervention as a comparison. Results: Baseline data identified 50 patients that were discharged from medicine teams. Average length of stay was 13.5 days. 19 patients (38%) had follow-up appointments made at time of discharge. Average time from discharge to appointment date was 15 days. After the intervention, 58 patients were discharged from medicine teams. Average length of stay was 4.8 days. 36 patients (62%) had follow-up appointments made at time of discharge. Average time of discharge to appointment date was 8 days. Conclusions: By providing internal medicine residents with contact information for local clinics, we were able to increase the percentage of patients who have appointments made at time of discharge by 24%. Results also show a decrease in the average time from discharge to appointment date, indicating timelier follow-up. The next steps will be to determine if this correlates to a reduction in readmission rates, which are costly to both the hospital and the patient

An assessment of Lot Quality Assurance Sampling to evaluate malaria outcome indicators: extending malaria indicator surveys.

BACKGROUND Large investments and increased global prioritization of malaria prevention and treatment have resulted in greater emphasis on programme monitoring and evaluation (M&E) in many countries. Many countries currently use large multistage cluster sample surveys to monitor malaria outcome indicators on a regional and national level. However, these surveys often mask local-level variability important to programme management. Lot Quality Assurance Sampling (LQAS) has played a valuable role for local-level programme M&E. If incorporated into these larger surveys, it would provide a comprehensive M&E plan at little, if any, extra cost. METHODS The Mozambique Ministry of Health conducted a Malaria Indicator Survey (MIS) in June and July 2007. We applied LQAS classification rules to the 345 sampled enumeration areas to demonstrate identifying high- and low-performing areas with respect to two malaria program indicators-'household possession of any bednet' and 'household possession of any insecticide-treated bednet (ITN)'. RESULTS As shown by the MIS, no province in Mozambique achieved the 70% coverage target for household possession of bednets or ITNs. By applying LQAS classification rules to the data, we identify 266 of the 345 enumeration areas as having bednet coverage severely below the 70% target. An additional 73 were identified with low ITN coverage. CONCLUSIONS This article demonstrates the feasibility of integrating LQAS into multistage cluster sampling surveys and using these results to support a comprehensive national, regional and local programme M&E system. Furthermore, in the recommendations we outlined how to integrate the Large Country-LQAS design into macro-surveys while still obtaining results available through current sampling practices

Predictors of Clostridium difficile infection-related mortality among older adults

BACKGROUND: Over 90% of annual deaths caused by Clostridium difficile infection (CDI) occur in persons aged ≥65 years. However, no large-scale studies have been conducted to investigate predictors of CDI-related mortality among older adults. METHODS: This case-control study included 540 CDI patients aged ≥60 years admitted to a tertiary care hospital in Detroit, Michigan, between January 2005 and December 2012. Cases were CDI patients who died within 30 days of CDI date. Controls were CDI patients who survived \u3e30 days after CDI date. Cases were matched to controls on a 1:3 ratio based on age and hospital acquisition of CDI. RESULTS: One-hundred and thirty cases (25%) were compared with 405 controls (75%). Independent predictors of CDI-related mortality included admission from another acute hospital (odds ratio [OR], 8.25; P = .001) or a long-term care facility (OR, 13.12; P = .012), McCabe score ≥2 (OR, 12.19; P \u3c .001), and high serum creatinine (≥1.7 mg/dL) (OR, 3.43; P = .021). The regression model was adjusted for the confounding effect of limited activity of daily living score, total number of antibiotic days prior to CDI, ileus on abdominal radiograph, low albumin (≤2.5 g/dL), elevated white blood cell count (\u3e15 × 1,000/mm3), and admission to intensive care unit because of CDI. CONCLUSIONS: Predictors of CDI-related mortality reported in this study could be applied to the development of a bedside scoring system for older adults with CDI

Location of Influenza Surveillance Sentinel sites and documented pH1N1 virus spread in Rwanda.

<p>From October 2009 - May 2010.</p

Clinical characteristics of positive and negative influenza cases during pandemic period in Rwanda from October 2009– May 2010.

<p>Clinical characteristics of positive and negative influenza cases during pandemic period in Rwanda from October 2009– May 2010.</p

Laboratory-confirmed A (H1N1) pdm09 by response phase and collection site in Rwanda from October 2009– May 2010.

*<p>n = 6; Districts with an ISS site;</p>±<p>n = 24; Districts without an ISS site.</p

Demographic and epidemiological characteristics of influenza cases in Rwanda from July 2008– May 2010.

<p>Demographic and epidemiological characteristics of influenza cases in Rwanda from July 2008– May 2010.</p